If you follow Zero Bug Tolerance of course you’re not supposed to have any bugs to fix after the code is done. But let’s get real. Is there any way to know how many bugs you're missing and will have to fix later, and how many bugs you might already have in your code? Are there any industry measures of code quality that you can use as a starting point?
For questions like these, the first place I look is one of the books by Capers Jones: arguably the leading expert in all things to do with software development metrics. There’s Applied Software Measurement, or Estimating Software Costs, or Software Engineering Best Practices: Lessons from Successful Projects in the Top Companies. These books offer different views into a fascinating set of data that Capers Jones has collected over decades from thousands of different projects. It’s easy to spend hours getting lost in this data set, and reading through and questioning the findings that he draws from it.
So what can we learn from Capers Jones about bugs and defect potentials and defect density rates? A lot, actually.
On average 85% of bugs introduced in design and development are caught before the code is released (this is the average in the US as of 2009). His research shows that this defect removal rate has stayed roughly the same over 20 years, which is disappointing given the advances in tools and methods over that time.
We introduce 5 bugs per Function Point (Capers Jones is awfully fond of measuring everything by Function Points, which are an abstract way of measuring code size), depending on the type of system being built. Web systems are a bit lower surprisingly, at 4 bugs per Function Point; other internal business systems are 5, military systems average around 7. Using backfiring (a crude technique to convert function points back into LOC measures) you can equivalence 1 Function Point to about 50-55 lines of Java code.
For the sake of simplicity, let’s use 1 Function Point = 50 LOC, and keep in mind that all of these numbers are really rough, and that using backfiring techniques to translate Function Points to source code statements introduces a probability of error, but it’s a lot easier than trying to think in Function Points. And all I want here is a rough indicator of how much trouble a team might be in.
If 85% of bugs are hopefully found and fixed before the code is released, this leaves 0.75 bugs per Function Point unfound (and obviously unfixed) in the code when it gets to production. Which means that for a small application of 1,000 Function Points (50,000 or so lines of Java code), you could expect around 750 defects at release .
And this is only accounting for the bugs that you don’t already know about: a lot of code is released with a list of known bugs that the development team hasn’t had a chance to fix, or doesn’t think is worth fixing, or doesn’t know how to fix. And, this is just your code: it doesn’t account for bugs in the technology stack that the application depends on: the frameworks and application platform, database and messaging middleware, and any open source libraries or COTS that you take advantage of.
Of these 750+ bugs around 25% will be severity 1 show stoppers – real production problems that cause something significant to break.
Ouch – no wonder most teams will spend a lot of time on support and fixing bugs after releasing a big system. Of course, if you’re building and releasing software incrementally, you’ll find and fix more of these bugs as you go along, but you’ll still be fixing a lot of bugs in production.
Remember that these are rough averages. And remember (especially the other guys out there), we can’t all be above average, no matter how much we would like to be. For risk management purposes, it might be best to stick with averages, or even consider yourself below the bar.
Also keep in mind that defect potentials increase with the size of the system – big apps have more bugs on average. Not only is there a higher potential to write buggy code in bigger systems, but as the code base gets bigger and more complex it’s also harder to find and fix bugs. So big systems get released with even more bugs, and really big apps with a lot more bugs.
All of this gets worse in maintenance
In maintenance, the average defect potential for making changes is higher than in development, about 6 bugs per Function Point instead of 5. And the chance of finding and fixing mistakes in your changes is lower (83%). This is all because it’s harder to work with legacy code that you didn’t write and don’t understand all that well. So you should expect to release 1.08 bugs per Function Point when changing code in maintenance, instead of 0.75 bugs per Function Point.
And maintenance teams still have to deal with the latent bugs in the system, some of which may hide in the code for years, or forever. This includes heisenbugs and ghosts and weird timing issues and concurrency problems that disappear when you try to debug them. On average, 50% of residual latent defects are found each calendar year. The more people using your code, the faster that these bugs will be found.
Of course, once you find these bugs, you still have to fix them. The average maintenance programmer can be expected to fix around 10 bugs per month – and maybe implement some small enhancements too. That's not a great return on investment.
Then there’s the problem of bug re-injections, or regressions – when a programmer breaks something accidentally as a side-effect of making a fix. On average, programmers fixing a bug will introduce a new bug 7% of the time – and this can run as high as 20% for complex, poorly-structured code. Trying to fix these bad fixes is even worse – programmers trying to fix these mistakes have a 15% chance of still messing up the fix, and a 30% chance of introducing yet another bug as a side effect! It's better to roll-back the fix and start again.
Unfortunately, all of this gets worse over time. Unless you are doing a perfect job of refactoring and continuously simplifying the code, you can expect code complexity to increase an average of between 1% and 3% per year. And most systems get bigger over time, as you add more features and copy-and-paste code (of course you don't do that): the code base for a system under maintenance increases between 5-10% per year. As the code gets bigger and more complex, the chance for more bugs also increases each year.
But what if we’re not average? What if we’re best in class?
What if you are doing an almost perfect job, if you are truly best in class? Capers Jones finds that best in class teams create half as many bugs as average teams (2.5 or fewer defects per Function Point instead of 5), and they find and fix 95% or more of these bugs before the code is released. That sounds impressive - it means only 0.125 bugs per Function Point. But for a 50,000 LOC system, that’s still somewhere around 125 bugs on delivery.
And as for zero bugs? In his analysis of 13,000 projects over a period of more than 40 years, there were 2 projects with no defects reported within a year of release. So you can aspire to it. But don’t depend on it.
Wednesday, August 24, 2011
Monday, August 15, 2011
The C14N challenge
Failing to properly validate input data is behind at least half of all application security problems. In order to properly validate input data, you have to start by first ensuring that all data is in the same standard, simple, consistent format – a canonical form. This is because of all the wonderful flexibility in internationalization and data formatting and encoding that modern platforms and especially the Web offer. Wonderful capabilities that attackers can take advantage of to hide malicious code inside data in all sorts of sneaky ways.
Canonicalization is a conceptually simple idea: take data inputs, and convert all of it into a single, simple, consistent normalized internal format before you do anything else with it. But how exactly do you do this, and how do you know that it has been done properly? What are the steps that programmers need to take to properly canonicalize data? And how do you test for it? This is where things get fuzzy as hell.
To read my latest post on canonicalization problems (and the search for solutions), go to the SANS Application Security blog.
Monday, July 11, 2011
Developing and Testing in the Cloud
There’s a lot of hype around “the Cloud” and what it can do.
One of the things that I am interested in is Cloud solutions that can help small software companies, and especially to kickstart software startups. Good tools that development teams can take advantage of to build and test their own stuff, without all of the hassle and expense of internal IT, buying and provisioning their own gear, setting up the tools and systems and networks and finding someone who understands all of it well enough to do it right and to keep it running. I want to find Cloud-based technology that can do for software teams what Salesforce.com does for customer-service businesses, so that software teams can get started off quickly, and doing things right from the start.
Software teams need a core set of shared tools and capabilities:
Managing and Building Code in the Cloud
I keep running into people using GitHub to host their source code repositories. Not just people working on Open Source projects, but companies using GitHub to manage commercial projects in hosted private repositories, a service used by a number of startups. In GitHub, developers have a platform for managing code with the Git distributed version control system, and they get access to a good set of tools for a development team, especially distributed teams: admin functions to control permissioning, wikis, a bug tracking system and an online code review tool.
One alternative to GitHub is BitBucket which uses the Mercurial DVCS. Like GitHub, BitBucket can be used to manage Open Source and private projects, and it looks like it offers a similar set of management capabilities and tools. GitHub is free for Open Source projects and cheap for small teams while BitBucket’s pricing is based on the number of users, small teams (up to 5 users) are free for open or closed source.
Another On Demand SCM platform built on Mercurial is Fog Creek Software’s Kiln which integrates nicely with Fog Creek’s bug tracking and planning system FogBugz.
Atlassian, which bought BitBucket last year also offers Jira Studio a comprehensive hosted development platform centered on a Subversion code repository integrated with the rest of Atlaissian’s strong development toolset: Jira bug tracking, FishEye for code searching, Confluence wiki, Greenhopper for Agile team planning, Elastic Bamboo for build and Continuous Integration on Amazon EC2 and Crucible for online code review. That’s almost everything that a team needs (all that is missing is static analysis and a functional and load testing platform). Presumably the same integrated development tool support will extend to BitBucket over time, although there are already some overlaps between BitBucket and Jira Studio.
And there’s CloudBees Dev@cloud which offers a choice of secure Git, SVN or Maven repositories, and Continuous Integration through a hosted Jenkins server.
Spring Source’s Code2Cloud might be a good option especially for Java / Spring developers when it is ready – the details are fuzzy at the moment.
Google Code is cool, but for now is only available for Open Source projects.
For enterprises, IBM recently announced Smart Business Development and Test Cloud and IBM Smart Business Development and Test on the IBM Cloud (also known as SmartCloud Enterprise - I’m not sure what the difference is between them, I got too worn out reading through the marketing speak) , with all sorts of IBM technology and partner tools. This stuff isn’t for the faint of heart, or the small of wallet.
Continuous Integration in the Cloud
Continuous Integration is a problem that development teams need to solve before they get too far along, and setting up a CI server and keeping it running isn’t trivial. It’s another good fit for the elastic On Demand model of the Cloud, paying for more infrastructure only when you need it.
Besides Atlassian’s hosted end-to-end platform, and CloudBees’ Jenkins as a Service which can be used to build code hosted on a repository accessible through the Internet, there are a few options for CI in the Cloud (courtesy of Pascal Thivent on Stack Overflow):
For Open Source projects there is CodeBetter based on the TeamCity build server. For proprietary projects there is Mike CI which is hosted on Amazon EC2, and includes interfaces to Subversion, Git and Mercurial repositories; and CI Foundry. However, I don’t know how real these services are. Mike CI was down the last couple of times I went to check on it, and CI Foundry’s home page includes this not-very-convincing testimonial:
Bug Tracking in the Cloud
There are some decent and reasonably priced Cloud-based bug tracking systems suitable for small teams, including Fog Creek Software’s FogBugz and Atlassian’s Jira Hosted option both of which are also included in their hosted code management suites.
Testing in the Cloud
The Cloud has a lot of promise for app testing, especially for large-scale performance and load testing. You’re building a cool new Web platform that needs to support 10,000 or 100,000 concurrent sessions according to your business plan. Before your investors put more money in, they want to see just how real your software is, how far away you are from delivering something that could work. Of course you don’t have the money or time or IT skills to build a large-scale test center of your own at this point, and there’s a lot of waste in doing this – you need a lot of gear, but you won’t need to use all of it very often.
You can spin up a test farm on EC2 surprisingly quickly, even a big one – and as long as you don’t need to run the tests for a long time and you don’t ask for too high a service level, it won’t cost you much at all. You pay for what you need when you need it.
To run stress tests you need more than just the app deployed in a farm. You also need a scalable stress test harness to drive load scenarios and to measure the results. There are some viable options available in the Cloud today: SOASTA with its CloudTest solution for load testing and extreme stress testing, scaling from smallish sites to really big; HP Load Runner in the Cloud running on EC2; JMeter in the Cloud; LoadStorm and BrowserMob.
There are also some other interesting test capabilities, including technology from Sauce Labs which lets you run Selenium tests on Web apps in the Cloud or manually test your app against different Cloud-based instantly-available browsers, all with video records of failed tests and other problems found. Simple, useful and cool.
A different take on Cloud-based testing that could work for startups and smaller companies is crowd-sourced testing services like uTest, where you get people “in the Cloud” to test your app (web apps, desktop apps and mobile apps), including functional coverage and regression testing (you give them tests, they run them and maybe help improve them), exploratory testing, usability testing, load testing and test planning and management. Like other Cloud-based On Demand solutions, you pay for work when you need it. uTest has a community of something like 40,000 testers available, although it’s not clear how many of them are much good.
Mob4Hire is another crowd-sourced solution specifically for mobile apps – they offer functional testing and usability testing and other services like market research.
Static Analysis in the Cloud
Some of the leading static analysis suppliers have also gone to the Cloud. HP offers Fortify on Demand to scan code for security vulnerabilities, and IBM has Rational Appscan OnDemand which includes assistance from an experienced team to help you interpret the results and plan remediation. Both of these solutions are targeted more towards the enterprise than small teams.
Veracode, which runs binary code analysis for security vulnerabilities and now also offers web vulnerability scanning, is only available through the Cloud. They offer a free trial scan of small Java apps for XSS and SQL Injection flaws (two of the most common and fatal web application vulnerabilities).
For startups that care about building reliable and secure apps (and most of them should, especially Web 2.0 startups and mobile app developers) it probably makes more sense to look at simpler developer IDE-based tools like Findbugs and Google’s CodePro Analytix (both free) or Klocwork Solo for Java which is priced per user.
The Tradeoffs
Managing your code, building it and testing it in the Cloud has a number of clear advantages:
The concern with using a Cloud platform like this comes down to some fundamental points:
1. It’s a shared platform, used by a lot of companies. The more successful the platform is, the more customers and interesting and valuable data / IP that they manage, the more attractive a target they are to bad guys. A small company by itself is an easy target, but not especially interesting or easy to find. But a platform that holds data for a lot of small, innovative companies? Yummy….[pronounced in a deep, growly voice with a foreign accent]
2. You take on some important risks any time that you outsource something that is core and critical to your business. Your data / code / customer base is more important to you than it is to whoever you outsource this responsibility to. If something goes badly wrong, if you can’t get an important change or an important bug fix out to a customer because the Cloud service isn’t working, or if the integrity or confidentiality of your code is compromised, they will be sad, and they will lose a customer (you)... but you may go out of business.
You can ask for, and pay for, good SLAs - but Amazon’s major EC2 outage earlier this year showed that even the best providers can’t meet their SLA commitments. While Amazon did a lot of things right in handling and recovering from that outage, it affected a lot of customers for too long.
3. There’s more to a secure online platform than using SSL and network firewalls and running in a good data center - pretending otherwise is at best naïve. As a customer, you need to be confident in how the Cloud services provider designed and built the software architecture and platform to protect the confidentiality and integrity of your IP and data, in their multi-tenant partitioning scheme and software security controls, and in their SDLC. Security vulnerabilities in application code are a serious source of risk to businesses on the Web, and most companies still do a poor job of building apps in a secure way.
DropBox, a Cloud-based document storage facility is an example of a platform that seemed to be secure and confidential, until somebody started to look deeper into it, and they continue to run into problems with basic security issues Unfortunately, none of the hosted platforms provide any kind of statement on their secure SDLC that I could find, on what steps they take to ensure that the platform is designed and implemented safely.
Testing in the Cloud, especially load testing for Web apps, looks to be a no-brainer – the business case is too compelling to ignore. Many of the other tools have a lot of promise: something like Atlassian’s end-to-end hosted studio would save a lot of time and trouble for a small company, and the upfront cost savings are real.
It comes down to how much trust you can put into your Cloud provider and their SLAs for availability and support, and whether in the end you can afford to trust your core IP to somebody else. 37 Signals, which builds and operates its own Cloud solutions for project management and document sharing, doesn’t think it is a good idea:
One of the things that I am interested in is Cloud solutions that can help small software companies, and especially to kickstart software startups. Good tools that development teams can take advantage of to build and test their own stuff, without all of the hassle and expense of internal IT, buying and provisioning their own gear, setting up the tools and systems and networks and finding someone who understands all of it well enough to do it right and to keep it running. I want to find Cloud-based technology that can do for software teams what Salesforce.com does for customer-service businesses, so that software teams can get started off quickly, and doing things right from the start.
Software teams need a core set of shared tools and capabilities:
- Source code management / version control
- Bug tracking
- Collaboration and shared documentationBuild and Continuous Integration
- Source code scanning and static analysis
- Unit and functional testing
- System, load and stress testing
- Code deployment.
Managing and Building Code in the Cloud
I keep running into people using GitHub to host their source code repositories. Not just people working on Open Source projects, but companies using GitHub to manage commercial projects in hosted private repositories, a service used by a number of startups. In GitHub, developers have a platform for managing code with the Git distributed version control system, and they get access to a good set of tools for a development team, especially distributed teams: admin functions to control permissioning, wikis, a bug tracking system and an online code review tool.
One alternative to GitHub is BitBucket which uses the Mercurial DVCS. Like GitHub, BitBucket can be used to manage Open Source and private projects, and it looks like it offers a similar set of management capabilities and tools. GitHub is free for Open Source projects and cheap for small teams while BitBucket’s pricing is based on the number of users, small teams (up to 5 users) are free for open or closed source.
Another On Demand SCM platform built on Mercurial is Fog Creek Software’s Kiln which integrates nicely with Fog Creek’s bug tracking and planning system FogBugz.
Atlassian, which bought BitBucket last year also offers Jira Studio a comprehensive hosted development platform centered on a Subversion code repository integrated with the rest of Atlaissian’s strong development toolset: Jira bug tracking, FishEye for code searching, Confluence wiki, Greenhopper for Agile team planning, Elastic Bamboo for build and Continuous Integration on Amazon EC2 and Crucible for online code review. That’s almost everything that a team needs (all that is missing is static analysis and a functional and load testing platform). Presumably the same integrated development tool support will extend to BitBucket over time, although there are already some overlaps between BitBucket and Jira Studio.
And there’s CloudBees Dev@cloud which offers a choice of secure Git, SVN or Maven repositories, and Continuous Integration through a hosted Jenkins server.
Spring Source’s Code2Cloud might be a good option especially for Java / Spring developers when it is ready – the details are fuzzy at the moment.
Google Code is cool, but for now is only available for Open Source projects.
For enterprises, IBM recently announced Smart Business Development and Test Cloud and IBM Smart Business Development and Test on the IBM Cloud (also known as SmartCloud Enterprise - I’m not sure what the difference is between them, I got too worn out reading through the marketing speak) , with all sorts of IBM technology and partner tools. This stuff isn’t for the faint of heart, or the small of wallet.
Continuous Integration in the Cloud
Continuous Integration is a problem that development teams need to solve before they get too far along, and setting up a CI server and keeping it running isn’t trivial. It’s another good fit for the elastic On Demand model of the Cloud, paying for more infrastructure only when you need it.
Besides Atlassian’s hosted end-to-end platform, and CloudBees’ Jenkins as a Service which can be used to build code hosted on a repository accessible through the Internet, there are a few options for CI in the Cloud (courtesy of Pascal Thivent on Stack Overflow):
For Open Source projects there is CodeBetter based on the TeamCity build server. For proprietary projects there is Mike CI which is hosted on Amazon EC2, and includes interfaces to Subversion, Git and Mercurial repositories; and CI Foundry. However, I don’t know how real these services are. Mike CI was down the last couple of times I went to check on it, and CI Foundry’s home page includes this not-very-convincing testimonial:
What people are saying...Thivent suggests that you can roll-your- own CI solution on Amazon EC2 or maybe use something like “CI in a box”. Using an On Demand platform like EC2 makes sense for CI, rather than provisioning your own gear: it’s a bit more work for you to do the setup, but you can rely on Amazon’s infrastructure to reduce costs and simplify operations.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Mauris quis ligula vel ligula varius cursus tristique eget sem. Mauris sagittis, ante id imperdiet auctor, sem mi condimentum neque, quis feugiat quam dui dictum risus.
- Chris Read
Continuous Deployment expert
Bug Tracking in the Cloud
There are some decent and reasonably priced Cloud-based bug tracking systems suitable for small teams, including Fog Creek Software’s FogBugz and Atlassian’s Jira Hosted option both of which are also included in their hosted code management suites.
Testing in the Cloud
The Cloud has a lot of promise for app testing, especially for large-scale performance and load testing. You’re building a cool new Web platform that needs to support 10,000 or 100,000 concurrent sessions according to your business plan. Before your investors put more money in, they want to see just how real your software is, how far away you are from delivering something that could work. Of course you don’t have the money or time or IT skills to build a large-scale test center of your own at this point, and there’s a lot of waste in doing this – you need a lot of gear, but you won’t need to use all of it very often.
You can spin up a test farm on EC2 surprisingly quickly, even a big one – and as long as you don’t need to run the tests for a long time and you don’t ask for too high a service level, it won’t cost you much at all. You pay for what you need when you need it.
To run stress tests you need more than just the app deployed in a farm. You also need a scalable stress test harness to drive load scenarios and to measure the results. There are some viable options available in the Cloud today: SOASTA with its CloudTest solution for load testing and extreme stress testing, scaling from smallish sites to really big; HP Load Runner in the Cloud running on EC2; JMeter in the Cloud; LoadStorm and BrowserMob.
There are also some other interesting test capabilities, including technology from Sauce Labs which lets you run Selenium tests on Web apps in the Cloud or manually test your app against different Cloud-based instantly-available browsers, all with video records of failed tests and other problems found. Simple, useful and cool.
A different take on Cloud-based testing that could work for startups and smaller companies is crowd-sourced testing services like uTest, where you get people “in the Cloud” to test your app (web apps, desktop apps and mobile apps), including functional coverage and regression testing (you give them tests, they run them and maybe help improve them), exploratory testing, usability testing, load testing and test planning and management. Like other Cloud-based On Demand solutions, you pay for work when you need it. uTest has a community of something like 40,000 testers available, although it’s not clear how many of them are much good.
Mob4Hire is another crowd-sourced solution specifically for mobile apps – they offer functional testing and usability testing and other services like market research.
Static Analysis in the Cloud
Some of the leading static analysis suppliers have also gone to the Cloud. HP offers Fortify on Demand to scan code for security vulnerabilities, and IBM has Rational Appscan OnDemand which includes assistance from an experienced team to help you interpret the results and plan remediation. Both of these solutions are targeted more towards the enterprise than small teams.
Veracode, which runs binary code analysis for security vulnerabilities and now also offers web vulnerability scanning, is only available through the Cloud. They offer a free trial scan of small Java apps for XSS and SQL Injection flaws (two of the most common and fatal web application vulnerabilities).
For startups that care about building reliable and secure apps (and most of them should, especially Web 2.0 startups and mobile app developers) it probably makes more sense to look at simpler developer IDE-based tools like Findbugs and Google’s CodePro Analytix (both free) or Klocwork Solo for Java which is priced per user.
The Tradeoffs
Managing your code, building it and testing it in the Cloud has a number of clear advantages:
- Faster time to market – you can get access to the infrastructure and tools quickly, with minimal hassle and setup time.
- Savings on cost and Capex – especially for load testing – you don’t have to pay upfront, you only have to pay for what you use when you use it, so you can better manage your cash flow.
- Convenience – you don’t have to provision and operate the gear yourself and add to your operational headaches.
- Access to specialist skills – your Cloud provider will know more about these technologies and how to solve a specific problem much better than you can afford to, so you don’t need to contract or hire gurus to help solve problems or make sure everything is setup properly, or waste time figuring it out yourself.
- Service levels – they’ll have more people to help keep things running, to make sure code and data are backed up, systems are cleaned up and monitored and patched. They’re less likely to lose your code and data than you are.
The concern with using a Cloud platform like this comes down to some fundamental points:
1. It’s a shared platform, used by a lot of companies. The more successful the platform is, the more customers and interesting and valuable data / IP that they manage, the more attractive a target they are to bad guys. A small company by itself is an easy target, but not especially interesting or easy to find. But a platform that holds data for a lot of small, innovative companies? Yummy….[pronounced in a deep, growly voice with a foreign accent]
2. You take on some important risks any time that you outsource something that is core and critical to your business. Your data / code / customer base is more important to you than it is to whoever you outsource this responsibility to. If something goes badly wrong, if you can’t get an important change or an important bug fix out to a customer because the Cloud service isn’t working, or if the integrity or confidentiality of your code is compromised, they will be sad, and they will lose a customer (you)... but you may go out of business.
You can ask for, and pay for, good SLAs - but Amazon’s major EC2 outage earlier this year showed that even the best providers can’t meet their SLA commitments. While Amazon did a lot of things right in handling and recovering from that outage, it affected a lot of customers for too long.
3. There’s more to a secure online platform than using SSL and network firewalls and running in a good data center - pretending otherwise is at best naïve. As a customer, you need to be confident in how the Cloud services provider designed and built the software architecture and platform to protect the confidentiality and integrity of your IP and data, in their multi-tenant partitioning scheme and software security controls, and in their SDLC. Security vulnerabilities in application code are a serious source of risk to businesses on the Web, and most companies still do a poor job of building apps in a secure way.
DropBox, a Cloud-based document storage facility is an example of a platform that seemed to be secure and confidential, until somebody started to look deeper into it, and they continue to run into problems with basic security issues Unfortunately, none of the hosted platforms provide any kind of statement on their secure SDLC that I could find, on what steps they take to ensure that the platform is designed and implemented safely.
Testing in the Cloud, especially load testing for Web apps, looks to be a no-brainer – the business case is too compelling to ignore. Many of the other tools have a lot of promise: something like Atlassian’s end-to-end hosted studio would save a lot of time and trouble for a small company, and the upfront cost savings are real.
It comes down to how much trust you can put into your Cloud provider and their SLAs for availability and support, and whether in the end you can afford to trust your core IP to somebody else. 37 Signals, which builds and operates its own Cloud solutions for project management and document sharing, doesn’t think it is a good idea:
We host all the source code for our applications internally for obvious security reasons. That’s not to say Github’s private repository hosting isn’t a good option, especially if you want a hassle-free setup. It’s just not for us.For some companies, especially startups, IP embodied in their code is critically important – it may be all that they have. Ironically, it’s these companies that are most likely to use a Cloud platform like GitHub or Jira Studio or BitBucket. Deciding whether to trust your future to the Cloud is not an easy decision to make. But as the technology continues to get better, it’s a question that is worth asking.
JH 22 Aug 08
Friday, July 1, 2011
Please, no more Manifestos
For some reason, people involved in software development have a thing for Manifestos (always with a Capital M). It all started with the Agile Manifesto. Then came the Software Craftsmanship Manifesto, signed by serious programmers big and small, except ironically the original author of Software Craftsmanship.
Then there have been other, less practical and less successful manifestos, like the Rugged Software Manifesto which as I have explained before was proclaimed with the naïve understanding that reciting feel-good phrases will somehow make developers write better software.
What brought this all up is that some people involved in DevOps still feel incomplete without a Manifesto of their very own.
This is a shame, because I like following what’s happening in DevOps and learning from the best (and worst) of it, and I want them to keep doing what they’re doing.
I don’t see the point in manifestos. They don’t move me or change the way that I think or work. I can get through each day without having to refer to a manifesto. I want tools and concrete ideas that I can use to get things done, to do a better job. Not motherhood or bullshit. Patterns and anti-patterns and recipes and best practices (and worst practices) – these are useful. But Manifestos? Useless, or at their worst, dangerous:
Manifestos s are a way to keep people from thinking and asking questions.
Or maybe Andrew Shafer is right
Then there have been other, less practical and less successful manifestos, like the Rugged Software Manifesto which as I have explained before was proclaimed with the naïve understanding that reciting feel-good phrases will somehow make developers write better software.
“I am rugged…”And the even-less-practical but much-more fun-to-read manifesto for truly hard-core programmers (there really is no problem that you can’t program your way out of) and of course the cynical Half-Arsed Agile Manifesto.
What brought this all up is that some people involved in DevOps still feel incomplete without a Manifesto of their very own.
This is a shame, because I like following what’s happening in DevOps and learning from the best (and worst) of it, and I want them to keep doing what they’re doing.
I don’t see the point in manifestos. They don’t move me or change the way that I think or work. I can get through each day without having to refer to a manifesto. I want tools and concrete ideas that I can use to get things done, to do a better job. Not motherhood or bullshit. Patterns and anti-patterns and recipes and best practices (and worst practices) – these are useful. But Manifestos? Useless, or at their worst, dangerous:
Workers of the world unite….Look how that one worked out.
Manifestos s are a way to keep people from thinking and asking questions.
Oh no, people have more than one way of thinking about a problem, or what something means!This is not a bad thing. This is a GOOD THING! This is how we move forward, this is how we get better. What we’re doing isn’t simple, or right, or wrong, so let’s stop pretending.
Or maybe Andrew Shafer is right
A manifesto, by definition, is essentially a marketing document.Either way, we don’t need more manifestos. What we need is people to keep thinking and asking questions, and getting things done.
“Re: Time for a DevOps Manifesto?”
Monday, June 27, 2011
Moving forward from Failure
System failures at scale are inescapable, as I have talked about before in the context of designing systems for failure in high-scale computing and how to apply these ideas to enterprise systems. Failures are wasted if you don’t learn enough from them; if the way that you design and deliver application systems, and the way that you deploy and run these systems, don’t get better as a result. You have to find lessons in each failure and constantly move forward, and make the system, and the team, more resilient.
I’ve read and heard a lot of good stuff over the last year or so from the DevOps community on handling system failures: how to minimize the risk of failures through deployment planning, how complex systems fail, how to communicate failures effectively to stakeholders so that you don’t destroy trust, and how to understand failures through postmortem analysis, including Jacob Loomis’ essay “How to Make Failure Beautiful” in the Web Operations book and John Allspaw’s keynote on Advanced Postmortem Fu and Human Error 101 at this year’s Velocity conference.
Many of these ideas fit with my own experience, and fill in some important gaps – they extend beyond the concerns of operating high-volume Web sites to broader, general problems in system operations and systems engineering, and deserve a wider audience outside of the Web operations and Web startup worlds.
Postmortems and Root Cause Analysis
The keys to successful postmortems and Root Cause Analysis are deceptively simple, and therefore difficult to get right:
Root Cause Analysis is important, but it is just the first step. Once you’ve reviewed a failure as a team and found your way to the root causes or at least identified some real problems, now you have to do something about it, beyond the immediate fixes and workarounds that the team made recovering from the incident.
There are straightforward things that you can do now – detective and corrective actions, quick fixes, low cost and low risk – so you do them. Fix a bug, plug a hole, add defensive coding, add some tests. Better logging and diagnostics and alerting, better error handling, better metrics – you can always do this stuff better – so that ops can see the problem coming or at least recognize it when it happens again. Better troubleshooting tools and training for ops.
Don’t stop with corrective actions either
But it’s still not enough to recover from a failure and patch the holes and tighten things up, or even build a better incident management capability. You have to make sure to find the lessons in each failure, really learn about why the failure happened, how it relates to other failures or problems that you have; and about how you dealt with the failure when it happened, and what you need to do to do a better job the next time. Then you have to act to reduce the risk and cost of failures, take steps to make sure that failures like this one, and even failures that aren’t like this one, don’t happen again. And that when the next failure does happen, that the team is more prepared for it, that you can recover faster and with less stress and impact to the business.
Some of these preventative actions are straightforward: training for developers and testers and ops, and better communications in and especially between teams, checklists, health checks and dependency checks, more disciplined change controls and deployment controls, and other safeties.
But most preventative changes, the ones that make a real difference, are deeper, more fundamental: fixing architecture, culture, organization, or management weaknesses. Fixing these kinds of problems takes longer, involves more people, and costs more. Making changes to the architecture – switching out a core technology or implementing a partitioning strategy to contain failures and to scale up – can take months to work out and implement. Organizational and management changes are hard because this directly impacts people’s jobs. Changing culture is even harder and takes longer, especially to make the changes stick.
You can’t improve what you can’t see – you need data
These problems are also hard to see and understand. It’s naïve to think that you can recognize or prove the need for fundamental changes to architecture or organization or management controls or culture from a postmortem meeting – it takes time and perspective and experience with more than one failure to see this kind of problem. And because the fixes are fundamental and expensive, they can be hard to justify, hard to get management and the business and your own people to buy-in.
You need data to help you understand what you’re doing wrong, what you need to change or what you need to stop doing – and what you’re doing right, what you need to keep doing, and to build a case for change. At last year’s Velocity conference, John Allspaw explained how to use metrics to find patterns and trends in failures, to understand what’s hurting you the most, or hurting you the most often, what’s working well and what’s not. To find out what changes are safe, and what changes aren’t; what failures are cheap and easy to recover from, and what failures can take the company down.
Metrics are important to help you see problems, and to help show if you are learning and getting better. Track the types and severity of failures, frequency of failures, your response to failures – time to detect and time to recover from failures; and the frequency and type and size of changes, and the correlation between changes and failures (by type and size of changes, by type and severity of failures). And make sure to track regressions – the number of times that you take a step backward.
Deciding what to fix, and what not to
Metrics can also help you to decide what you can fix and change, and what’s not worth fixing or changing – at least not for now. You need to recognize when a problem is a true outlier, an isolated case that is unlikely to happen again and that you can accept and move forward. It’s important that you can see the difference between a problem that is a one-of, and a first-of –an indicator of something deeply wrong, a fundamental weakness in the way that the system was built or the way that you work. You don’t want to over-react to outliers but you also don’t can’t treat every problem as unique and miss seeing the patterns and connections, the underlying root causes.
There is diminishing returns in preventing (or even reducing the probability) of some problems, trying to stop the unstoppable. Some problems are too rare, or too expensive or difficult to prevent – and trying to prevent these problems can introduce new complexities and new risks into the system. I agree with John Allspaw that there are situations when it makes more sense to focus on how to react and recover faster, on improving MTTR rather than MTBF, but you need to know when to make that case.
Moving forward from Failures is real work and needs to be managed
People don’t want to think about failures for long – they want to put the mistakes and confusion and badness behind them and move on. And the business and management want priorities back on day-to-day delivery as soon as possible. When failures happen, you need to act fast, get the most that you can out of the moment, make decisions and commitments and reinforce changes while the pain is still fresh.
But fixing real problems in the system or the way that people are organized or the way that they work or the way that they think, can’t be done right away. Building reliability and resilience and operability into how you work and how you think and how you build and test software and how you solve problems takes time and commitment and continuous reinforcement. Management, and the team, have to be made accountable for longer-term preventative actions, for making bigger changes and improvements.
This work needs to be recognized by the business and management and the team and built in to the backlog, and actively managed. You need to remind people of the risks when you see them missing steps or trying to cut corners, or falling back into old patterns of thinking and acting. You’ll need to use metrics and cost data to drive behaviour and to drive change, and to decide how much to push and how often: are you changing too much too often, running too loose; or is change costing you too much, are you overcompensating?
A Resilient system is a DevOps problem
Development can’t solve all of these problems on its own, and neither can Operations. It needs the commitment of development (and by development, I mean everyone responsible for designing, developing, testing, securing and releasing the software) and operations (everyone responsible for building and securing and running the infrastructure, deploying the code, and running and monitoring the system). This is a real DevOps problem: development and operations have to be aligned and work together, communicating and collaborating, sharing responsibilities and technology. This is one of the places where I see DevOps adding real value in any organization.
I’ve read and heard a lot of good stuff over the last year or so from the DevOps community on handling system failures: how to minimize the risk of failures through deployment planning, how complex systems fail, how to communicate failures effectively to stakeholders so that you don’t destroy trust, and how to understand failures through postmortem analysis, including Jacob Loomis’ essay “How to Make Failure Beautiful” in the Web Operations book and John Allspaw’s keynote on Advanced Postmortem Fu and Human Error 101 at this year’s Velocity conference.
Many of these ideas fit with my own experience, and fill in some important gaps – they extend beyond the concerns of operating high-volume Web sites to broader, general problems in system operations and systems engineering, and deserve a wider audience outside of the Web operations and Web startup worlds.
Postmortems and Root Cause Analysis
The keys to successful postmortems and Root Cause Analysis are deceptively simple, and therefore difficult to get right:
- get the right people together in a room.
- create the right environment for blameless problem solving: make it safe for people to be open and honest, don’t point fingers, focus on facts and solving problems and how to get better.
- postmortems are expensive and painful, and people want to get out of them as soon as possible. Don’t stop too soon, before the team has really understood the problems, and the solutions.
- don’t be satisfied with a single root cause – complex system failures usually have multiple causes if you dig deep enough.
- human error isn’t a root cause – it’s a symptom of something else that you’ve done wrong.
Root Cause Analysis is important, but it is just the first step. Once you’ve reviewed a failure as a team and found your way to the root causes or at least identified some real problems, now you have to do something about it, beyond the immediate fixes and workarounds that the team made recovering from the incident.
There are straightforward things that you can do now – detective and corrective actions, quick fixes, low cost and low risk – so you do them. Fix a bug, plug a hole, add defensive coding, add some tests. Better logging and diagnostics and alerting, better error handling, better metrics – you can always do this stuff better – so that ops can see the problem coming or at least recognize it when it happens again. Better troubleshooting tools and training for ops.
Don’t stop with corrective actions either
But it’s still not enough to recover from a failure and patch the holes and tighten things up, or even build a better incident management capability. You have to make sure to find the lessons in each failure, really learn about why the failure happened, how it relates to other failures or problems that you have; and about how you dealt with the failure when it happened, and what you need to do to do a better job the next time. Then you have to act to reduce the risk and cost of failures, take steps to make sure that failures like this one, and even failures that aren’t like this one, don’t happen again. And that when the next failure does happen, that the team is more prepared for it, that you can recover faster and with less stress and impact to the business.
Some of these preventative actions are straightforward: training for developers and testers and ops, and better communications in and especially between teams, checklists, health checks and dependency checks, more disciplined change controls and deployment controls, and other safeties.
But most preventative changes, the ones that make a real difference, are deeper, more fundamental: fixing architecture, culture, organization, or management weaknesses. Fixing these kinds of problems takes longer, involves more people, and costs more. Making changes to the architecture – switching out a core technology or implementing a partitioning strategy to contain failures and to scale up – can take months to work out and implement. Organizational and management changes are hard because this directly impacts people’s jobs. Changing culture is even harder and takes longer, especially to make the changes stick.
You can’t improve what you can’t see – you need data
These problems are also hard to see and understand. It’s naïve to think that you can recognize or prove the need for fundamental changes to architecture or organization or management controls or culture from a postmortem meeting – it takes time and perspective and experience with more than one failure to see this kind of problem. And because the fixes are fundamental and expensive, they can be hard to justify, hard to get management and the business and your own people to buy-in.
You need data to help you understand what you’re doing wrong, what you need to change or what you need to stop doing – and what you’re doing right, what you need to keep doing, and to build a case for change. At last year’s Velocity conference, John Allspaw explained how to use metrics to find patterns and trends in failures, to understand what’s hurting you the most, or hurting you the most often, what’s working well and what’s not. To find out what changes are safe, and what changes aren’t; what failures are cheap and easy to recover from, and what failures can take the company down.
Metrics are important to help you see problems, and to help show if you are learning and getting better. Track the types and severity of failures, frequency of failures, your response to failures – time to detect and time to recover from failures; and the frequency and type and size of changes, and the correlation between changes and failures (by type and size of changes, by type and severity of failures). And make sure to track regressions – the number of times that you take a step backward.
Deciding what to fix, and what not to
Metrics can also help you to decide what you can fix and change, and what’s not worth fixing or changing – at least not for now. You need to recognize when a problem is a true outlier, an isolated case that is unlikely to happen again and that you can accept and move forward. It’s important that you can see the difference between a problem that is a one-of, and a first-of –an indicator of something deeply wrong, a fundamental weakness in the way that the system was built or the way that you work. You don’t want to over-react to outliers but you also don’t can’t treat every problem as unique and miss seeing the patterns and connections, the underlying root causes.
There is diminishing returns in preventing (or even reducing the probability) of some problems, trying to stop the unstoppable. Some problems are too rare, or too expensive or difficult to prevent – and trying to prevent these problems can introduce new complexities and new risks into the system. I agree with John Allspaw that there are situations when it makes more sense to focus on how to react and recover faster, on improving MTTR rather than MTBF, but you need to know when to make that case.
Moving forward from Failures is real work and needs to be managed
People don’t want to think about failures for long – they want to put the mistakes and confusion and badness behind them and move on. And the business and management want priorities back on day-to-day delivery as soon as possible. When failures happen, you need to act fast, get the most that you can out of the moment, make decisions and commitments and reinforce changes while the pain is still fresh.
But fixing real problems in the system or the way that people are organized or the way that they work or the way that they think, can’t be done right away. Building reliability and resilience and operability into how you work and how you think and how you build and test software and how you solve problems takes time and commitment and continuous reinforcement. Management, and the team, have to be made accountable for longer-term preventative actions, for making bigger changes and improvements.
This work needs to be recognized by the business and management and the team and built in to the backlog, and actively managed. You need to remind people of the risks when you see them missing steps or trying to cut corners, or falling back into old patterns of thinking and acting. You’ll need to use metrics and cost data to drive behaviour and to drive change, and to decide how much to push and how often: are you changing too much too often, running too loose; or is change costing you too much, are you overcompensating?
A Resilient system is a DevOps problem
Development can’t solve all of these problems on its own, and neither can Operations. It needs the commitment of development (and by development, I mean everyone responsible for designing, developing, testing, securing and releasing the software) and operations (everyone responsible for building and securing and running the infrastructure, deploying the code, and running and monitoring the system). This is a real DevOps problem: development and operations have to be aligned and work together, communicating and collaborating, sharing responsibilities and technology. This is one of the places where I see DevOps adding real value in any organization.
Tuesday, June 21, 2011
Estimation is not a Black Art
Construx Software is one of the few companies that take the problems of estimation in software development seriously. Earlier this year I attended Steve McConnell’s Software Estimation in Depth course based on his book Software Estimation: Demystifying the Black Art. Two days reviewing factors in estimation and the causes of errors in estimating, the differences between estimates and targets and commitments, how to present estimates to the customer and to management, the importance of feedback and data, estimating techniques that work and why they work, and when and how to apply these techniques to different estimating problems.
You can get a lot out of reading Steve’s book, but the course setting gives you a chance to work with other people and solve real problems in estimation and planning, dig deep into questions with the man himself, and it introduces new material and research not included in the book. The key takeways from this course for me were:
The Basics
Even simple techniques work much better than “expert judgment” (aka wild ass guesses). With training and using straightforward tools and simple mathematical models and following a bit of structure, you can get to 70-75% accuracy in estimating, which is good enough for most of us.
Never estimate on the spot – it is professionally irresponsible, and you’re setting yourself, your team, and your customer up to fail. Always give the person who is doing the estimate time to think. Everyone knows this, but it’s important to be reminded of it, especially under pressure.
People’s systemic error tendency is to underestimate. Many organizations underestimate by a factor of at least 2x. This also applies to comparing back to actual costs: a common mistake is to remember how much you estimated something would take, not how long it actually took.
Estimates are always better if you can use real data to calibrate them: to compare estimates against evidence of how your organization has performed in the past. I knew this. But what I didn’t know is that you don’t need a lot of data points: even a few weeks of data can be useful, especially if it contradicts with judgment and forces you to question your assumptions.
Use different techniques at different stages as you move along a project’s Cone of Uncertainty, depending on how much information you have available at the time, and how high the stakes are – what the cost of estimation errors could be to the business. If you need higher confidence or higher quality estimates, use multiple techniques and compare the results.
I like T-Shirt sizing to help in planning and making a business case. Developers come up with a rough order of magnitude estimate on cost or effort (small, medium, large, extra-large) while the Customer/Product Owner/Product Manager does the same for the expected business value of a feature or change request. Then you match them up: Extra-Large business value and Small development cost gets a big thumbs-up. Small business value and Extra-Large cost gets a big thumbs down.
Estimating by analogy – comparing the work that you need to estimate to something similar that you’ve done before – is a simple technique that we use a lot. It works well if you are adding “another screen”, writing “another report”, or building “another interface”. It’s a good fit for maintenance, if you’ve been working with the same code for a while and know most of the gotchas.
Intact teams (like mine) tend to view problems in a similar way – ideas and experience converge. This is good if you work on similar problems, in maintenance or a long-running project, because you can make decisions quickly and confidently. But this is bad if you are confronted with new problems – you need techniques like Wideband Delphi to challenge people’s thinking patterns and let new ideas in.
Agile Estimating and Story Points and Velocity
We spent more time than I expected exploring issues in estimating Agile development work. Incremental and iterative planning in Agile development poses problems for a lot of organizations. The business/customer needs to know when the system will be ready and how much it will cost so that they can make their own commitments, but the team ideally wants to work this out iteratively, as they go. This means instead that they have to define the scope and cost as much as they can upfront, and then work out the details in each sprint – more like staged or spiral development methods. Once they have the backlog sized, they can plan out sprints based on forecast velocity or historical velocity - if they can figure this out.
I’m still not convinced that Agile story point estimating is better than (or as good as) other techniques, or that programmers sitting around a table playing Planning Poker is really an effective alternative to thoughtful estimating. Story points create some problems in planning new project development, because they are purposefully abstract – too abstract to be useful in helping to make commitments to the business. You might have an idea of how many story points give or take you have to deliver, but what’s a story point in the real world? People who use story points can’t seem to agree on how to define a story point, what a story point means and how or when to use them in estimating.
More fundamentally, you can’t know what a story point costs until the team starts to deliver, by measuring the team’s velocity (the actual story points completed in an iteration).This leaves you with a bootstrapping problem: you can’t estimate how long it is going to take to do something until you do it. You can look at data from other projects (if you’ve tracked this data), but you’re not supposed to compare story points across projects because each project and each team is unique, and their idea of story points may be different. So you have to make an educated guess as to how long it is going to take you to deliver what’s in front of you, and now we’re back to the “expert judgement” problem.
The good news is that it won’t take long to calibrate Story Point estimates against evidence from the team’s actual velocity. Mike Cohn in Succeeding with Agile says that you need at least 5 iterations to establish confidence in a team’s velocity. But Construx has found that you can have a good understanding of a team’s velocity in as little as 2 iterations. That’s not a long time to wait for some kind of answer on how long it should take to get the work that you know that you have in front of you done.
There is more to estimating
There is a lot more to estimating, and to this estimation course: details on different techniques and rules of thumb, models and software tools, how to measure estimation error, how to turn estimates into schedules, how to handle schedule compression and how to stay out of the Impossible Zone. To get a taste of the course, check out this webinar on the 10 Deadly Sins of Software Estimation.
Software estimation, like other problems in managing software development, doesn’t require a lot of advanced technical knowledge. It comes down to relatively simple ideas and practices that need to be consistently applied; to fundamentals and discipline. That doesn’t mean that it is easy to do it right, but it’s not dark magic either.
You can get a lot out of reading Steve’s book, but the course setting gives you a chance to work with other people and solve real problems in estimation and planning, dig deep into questions with the man himself, and it introduces new material and research not included in the book. The key takeways from this course for me were:
The Basics
Even simple techniques work much better than “expert judgment” (aka wild ass guesses). With training and using straightforward tools and simple mathematical models and following a bit of structure, you can get to 70-75% accuracy in estimating, which is good enough for most of us.
Never estimate on the spot – it is professionally irresponsible, and you’re setting yourself, your team, and your customer up to fail. Always give the person who is doing the estimate time to think. Everyone knows this, but it’s important to be reminded of it, especially under pressure.
People’s systemic error tendency is to underestimate. Many organizations underestimate by a factor of at least 2x. This also applies to comparing back to actual costs: a common mistake is to remember how much you estimated something would take, not how long it actually took.
Estimates are always better if you can use real data to calibrate them: to compare estimates against evidence of how your organization has performed in the past. I knew this. But what I didn’t know is that you don’t need a lot of data points: even a few weeks of data can be useful, especially if it contradicts with judgment and forces you to question your assumptions.
Use different techniques at different stages as you move along a project’s Cone of Uncertainty, depending on how much information you have available at the time, and how high the stakes are – what the cost of estimation errors could be to the business. If you need higher confidence or higher quality estimates, use multiple techniques and compare the results.
I like T-Shirt sizing to help in planning and making a business case. Developers come up with a rough order of magnitude estimate on cost or effort (small, medium, large, extra-large) while the Customer/Product Owner/Product Manager does the same for the expected business value of a feature or change request. Then you match them up: Extra-Large business value and Small development cost gets a big thumbs-up. Small business value and Extra-Large cost gets a big thumbs down.
Estimating by analogy – comparing the work that you need to estimate to something similar that you’ve done before – is a simple technique that we use a lot. It works well if you are adding “another screen”, writing “another report”, or building “another interface”. It’s a good fit for maintenance, if you’ve been working with the same code for a while and know most of the gotchas.
Intact teams (like mine) tend to view problems in a similar way – ideas and experience converge. This is good if you work on similar problems, in maintenance or a long-running project, because you can make decisions quickly and confidently. But this is bad if you are confronted with new problems – you need techniques like Wideband Delphi to challenge people’s thinking patterns and let new ideas in.
Agile Estimating and Story Points and Velocity
We spent more time than I expected exploring issues in estimating Agile development work. Incremental and iterative planning in Agile development poses problems for a lot of organizations. The business/customer needs to know when the system will be ready and how much it will cost so that they can make their own commitments, but the team ideally wants to work this out iteratively, as they go. This means instead that they have to define the scope and cost as much as they can upfront, and then work out the details in each sprint – more like staged or spiral development methods. Once they have the backlog sized, they can plan out sprints based on forecast velocity or historical velocity - if they can figure this out.
I’m still not convinced that Agile story point estimating is better than (or as good as) other techniques, or that programmers sitting around a table playing Planning Poker is really an effective alternative to thoughtful estimating. Story points create some problems in planning new project development, because they are purposefully abstract – too abstract to be useful in helping to make commitments to the business. You might have an idea of how many story points give or take you have to deliver, but what’s a story point in the real world? People who use story points can’t seem to agree on how to define a story point, what a story point means and how or when to use them in estimating.
More fundamentally, you can’t know what a story point costs until the team starts to deliver, by measuring the team’s velocity (the actual story points completed in an iteration).This leaves you with a bootstrapping problem: you can’t estimate how long it is going to take to do something until you do it. You can look at data from other projects (if you’ve tracked this data), but you’re not supposed to compare story points across projects because each project and each team is unique, and their idea of story points may be different. So you have to make an educated guess as to how long it is going to take you to deliver what’s in front of you, and now we’re back to the “expert judgement” problem.
The good news is that it won’t take long to calibrate Story Point estimates against evidence from the team’s actual velocity. Mike Cohn in Succeeding with Agile says that you need at least 5 iterations to establish confidence in a team’s velocity. But Construx has found that you can have a good understanding of a team’s velocity in as little as 2 iterations. That’s not a long time to wait for some kind of answer on how long it should take to get the work that you know that you have in front of you done.
There is more to estimating
There is a lot more to estimating, and to this estimation course: details on different techniques and rules of thumb, models and software tools, how to measure estimation error, how to turn estimates into schedules, how to handle schedule compression and how to stay out of the Impossible Zone. To get a taste of the course, check out this webinar on the 10 Deadly Sins of Software Estimation.
Software estimation, like other problems in managing software development, doesn’t require a lot of advanced technical knowledge. It comes down to relatively simple ideas and practices that need to be consistently applied; to fundamentals and discipline. That doesn’t mean that it is easy to do it right, but it’s not dark magic either.
Monday, June 6, 2011
Safer Software through Secure Frameworks
We have to make it easier for developers to build secure apps, especially Web apps. We can't keep forcing everybody who builds an application to understand and plug all of the stupid holes in how the Web works on their own — and to do this perfectly right every time. What we need is implementation-level security issues taken care of at the language and framework level. So that developers can focus on their real jobs: solving design problems and writing code that works.
Go to the SANS Application Security Street Fighter for my latest post on how to write safer software using secure frameworks, and application frameworks that are secure. And to read more about the OWASP Developer Outreach.
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