Is Copy and Paste Programming really a problem?

Copy and Paste Programming – taking a copy of existing code in your project and repurposing it – violates coding best practices like Don’t Repeat Yourself (DRY). It’s one of the most cited examples of technical debt, a lazy way of working, sloppy and short-sighted: an antipattern that adds to the long term cost of keeping a code base alive.

But it’s also a natural way to get stuff done – find something that already works, something that looks close to what you want to do, take a copy and use it as a starting point. Almost everybody has done in at some point. This is because there are times when copy and paste programming is not only convenient, but it might also be the right thing to do.

First of all, let’s be clear what I mean by copy and paste. This is not copying code examples off of the Internet, a practice that comes with its own advantages and problems. By copy and paste I mean when programmers take a shortcut in reuse – when they need to solve a problem that is similar to another problem in the system, they’ll start by taking a copy of existing code and changing what they need to.

Early in design and development, copy and paste programming has no real advantage. The code and design are still plastic, this is your chance to come up with the right set of abstractions, routines and libraries to do what the system needs to do. And there’s not a lot to copy from anyways. It’s late in development when you already have a lot of code in place, and especially when you are maintaining large, long-lived systems, that the copy and paste argument gets much more complicated.

Why Copy and Paste?

Programmers copy and paste because it saves time. First, you have a starting point, code that you know works. All you have to do is figure out what needs to be changed or added. You can focus on the problem you are trying to solve, on what is different, and you only need to understand what you are going to actually use. You are more free to iterate and make changes to fit the problem in front of you – you can cleanup code when you need to, delete code that you don’t need. All of this is important, because you may not know what you will need to keep, what you need to change, and what you don’t need at all until you are deeper into solving the problem.

Copy and paste programming also reduces risk. If you have to go back and change and extend existing code to do what it does today as well as to solve your new problem, you run the risk of breaking something that is already working. It is usually safer and less expensive (in the short term at least) to take a copy and work from there.

What if you are building a new B2B customer interface that will be used by a new set of customers? It probably makes sense to take an existing interface as a starting point, reuse the scaffolding and plumbing and wiring at least and as much of the the business code as makes sense, and then see what you need to change. In the end, there will be common code used by both interfaces (after all, that’s why you are taking a copy), but it could take a while before you know what this code is.

Finding a common design, the right abstractions and variations to support different implementations and to handle exceptions can be difficult and time consuming. You may end up with code that is harder to understand and harder to maintain and change in the future – because the original design didn’t anticipate the different exceptions and extensions, and refactoring can only take you so far. You may need a new design and implementation.

Changing the existing code, refactoring or rewriting some of it to be general-purpose, shared and extendable, will add cost and risk to the work in front of you. You can’t afford to create problems for existing customers and partners just because you want to bring some new customers online. You’ll need to be extra careful, and you’ll have to understand not only the details of what you are trying to do now (the new interface), but all of the details of the existing interface, its behavior and assumptions, so that you can preserve all of it.

It’s naïve to think that all of this behavior will be captured in your automated tests – assuming that you have a good set of automated tests. You’ll need to go back and redo integration testing on the existing interface. Getting customers and partners who may have already spent weeks or months to test the software to retest it is difficult and expensive. They (justifiably) won’t see the need to go through this time and expense because what they have is already working fine.

Copying and pasting now, and making a plan to come back later to refactor or even redesign if necessary towards a common solution, is the right approach here.

When Copy and Paste makes sense

In Making Software’s chapter on “Copy-Paste as a Principled Engineering Tool”, Michael Godfrey and Cory Kapser explore the costs of copy and paste programming, and the cases where copy and paste make sense:

1. Forking – purposely creating variants for hardware or platform variation, or for exploratory reasons.
2. Templating –some languages don’t support libraries and shared functions well so it may be necessary to copy and paste to share code. Somewhere back in the beginning of time, the first COBOL programmer wrote a complete COBOL program – everybody else after that copied and pasted from each other.
3. Customizing – creating temporary workarounds – as long as it is temporary.
4. Microsoft’s practice of “clone and own” to solve problems in big development organizations. One team takes code from another group and customizes it or adapts it to their own purposes – now they own their copy. This is a common approach with open source code that is used as a foundation and needs to be extended to solve a proprietary problem.

When Copy and Paste becomes a Problem

When to copy and paste, and how much of a problem it will become over time, depends on a few important factors.

First, the quality of what you are copying – how understandable the code is, how stable it is, how many bugs it has in it. You don’t want to start off by inheriting somebody else’s problems.

How many copies have been made. A common rule of thumb from Fowler and Beck`s Refactoring book is “three strikes and you refactor”. This rule comes from recognizing that by making a copy of something that is already working and changing the copy, you’ve created a small maintenance problem. It may not be clear what this maintenance problem is yet or how best to clean it up, because only two cases are not always enough to understand what is common and what is special.

But the more copies that you make, the more of a maintenance problem that you create – the cost of making changes and fixes to multiple copies, and the risk of missing making a change or fix to all of the copies increases. By the time that you make a third copy, you should be able to see patterns – what’s common between the code, and what isn’t. And if you have to do something in three similar but different ways, there is a good chance that there will be a fourth implementation, and a fifth. By the third time, it’s worthwhile to go back and restructure the code and come up with a more general-purpose solution.

How often you have to change the copied code and keep it in sync – particularly, how often you have to change or fix the same code in more than one place.

How well you know the code, do you know that there are clones and where to find them? How long it takes to find the copies, and how sure you are that you found them all. Tools can help with this. Source code analysis tools like clone detectors can help you find copy and paste code – outright copies and code that is not the same but similar (fuzzier matching with fuzzier results). Copied code is often fiddled with over time by different programmers, which makes it harder for tools to find all of the copies. Some programmers recommend leaving comments as markers in the code when you make a copy, highlighting where the copy was taken from, so that a maintenance programmer in the future making a fix will know to look for and check the other code.

Copy and Paste programming doesn’t come for free. But like a lot of other ideas and practices in software development, copy and paste programming isn’t right or wrong. It’s a tool that can be used properly, or abused.

Brian Foote, one of the people who first recognized the Big Ball of Mud problem in software design, says that copy and paste programming is the one form of reuse that programmers actually follow, because it works.

It’s important to recognize this. If we’re going to Copy and Paste, let's do a responsible job of it.

Defensive Programming: Being Just-Enough Paranoid

Hey, let’s be careful out there.
Sergeant Esterhaus, daily briefing to the force of Hill Street Blues

When developers run into an unexpected bug and can’t fix it, they’ll “add some defensive code” to make the code safer and to make it easier to find the problem. Sometimes just doing this will make the problem go away. They’ll tighten up data validation – making sure to check input and output fields and return values. Review and improve error handling – maybe add some checking around “impossible” conditions. Add some helpful logging and diagnostics. In other words, the kind of code that should have been there in the first place.

Expect the Unexpected

The whole point of defensive programming is guarding against errors you don’t expect.
Steve McConnell, Code Complete

The few basic rules of defensive programming are explained in a short chapter in Steve McConnell’s classic book on programming, Code Complete:

1. Protect your code from invalid data coming from “outside”, wherever you decide “outside” is. Data from an external system or the user or a file, or any data from outside of the module/component. Establish “barricades” or “safe zones” or “trust boundaries” – everything outside of the boundary is dangerous, everything inside of the boundary is safe. In the barricade code, validate all input data: check all input parameters for the correct type, length, and range of values. Double check for limits and bounds.
2. After you have checked for bad data, decide how to handle it. Defensive Programming is NOT about swallowing errors or hiding bugs. It’s about deciding on the trade-off between robustness (keep running if there is a problem you can deal with) and correctness (never return inaccurate results). Choose a strategy to deal with bad data: return an error and stop right away (fast fail), return a neutral value, substitute data values, … Make sure that the strategy is clear and consistent.
3. Don’t assume that a function call or method call outside of your code will work as advertised. Make sure that you understand and test error handling around external APIs and libraries.
4. Use assertions to document assumptions and to highlight “impossible” conditions, at least in development and testing. This is especially important in large systems that have been maintained by different people over time, or in high-reliability code.
5. Add diagnostic code, logging and tracing intelligently to help explain what’s going on at run-time, especially if you run into a problem.
6. Standardize error handling. Decide how to handle “normal errors” or “expected errors” and warnings, and do all of this consistently.
7. Use exception handling only when you need to, and make sure that you understand the language’s exception handler inside out.

Programs that use exceptions as part of their normal processing suffer from all the readability and maintainability problems of classic spaghetti code.
The Pragmatic Programmer

I would add a couple of other rules. From Michael Nygard’s Release It! never ever wait forever on an external call, especially a remote call. Forever can be a long time when something goes wrong. Use time-out/retry logic and his Circuit Breaker stability pattern to deal with remote failures.

And for languages like C and C++, defensive programming also includes using safe function calls to avoid buffer overflows and common coding mistakes.

Different Kinds of Paranoia

The Pragmatic Programmer describes defensive programming as “Pragmatic Paranoia”. Protect your code from other people’s mistakes, and your own mistakes. If in doubt, validate. Check for data consistency and integrity. You can’t test for every error, so use assertions and exception handlers for things that “can’t happen”. Learn from failures in test and production – if this failed, look for what else can fail. Focus on critical sections of code – the core, the code that runs the business.

Healthy Paranoid Programming is the right kind of programming. But paranoia can be taken too far. In the Error Handling chapter of Clean Code, Michael Feathers cautions that

“many code bases are dominated by error handling”

Too much error handling code not only obscures the main path of the code (what the code is actually trying to do), but it also obscures the error handling logic itself – so that it is harder to get it right, harder to review and test, and harder to change without making mistakes. Instead of making the code more resilient and safer, it can actually make the code more error-prone and brittle.

There’s healthy paranoia, then there’s over-the-top-error-checking, and then there’s bat shit crazy crippling paranoia – where defensive programming takes over and turns in on itself.

The first real world system I worked on was a “Store and Forward” network control system for servers (they were called minicomputers back then) across the US and Canada. It shared data between distributed systems, scheduled jobs, and coordinated reporting across the network. It was designed to be resilient to network problems and automatically recover and restart from operational failures. This was ground breaking stuff at the time, and a hell of a technical challenge.

The original programmer on this system didn’t trust the network, didn’t trust the O/S, didn’t trust Operations, didn’t trust other people’s code, and didn’t trust his own code – for good reason. He was a chemical engineer turned self-taught system programmer who drank a lot while coding late at night and wrote thousands of lines of unstructured FORTRAN and Assembler under the influence. The code was full of error checking and self diagnostics and error-correcting code, the files and data packets had their own checksums and file-level passwords and hidden control labels, and there was lots of code to handle sequence accounting exceptions and timing-related problems – code that mostly worked most of the time. If something went wrong that it couldn’t recover from, programs would crash and report a “label of exit” and dump the contents of variables – like today’s stack traces. You could theoretically use this information to walk back through the code to figure out what the hell happened. None of this looked anything like anything that I learned about in school. Reading and working with this code was like programming your way out of Arkham Asylum.

If the programmer ran into bugs and couldn’t fix them, that wouldn’t stop him. He would find a way to work around the bugs and make the system keep running. Then later after he left the company, I would find and fix a bug and congratulate myself until it broke some “error-correcting” code somewhere else in the network that now depended on the bug being there. So after I finally figured out what was going on, I took out as much of this “protection” as I could safely remove, and cleaned up the error handling so that I could actually maintain the system without losing what was left of my mind. I setup trust boundaries for the code – although I didn’t know that’s what it was called then – deciding what data couldn’t be trusted and what could. Once this was done I was able to simplify the defensive code so that I could make changes without the system falling over itself, and still protect the core code from bad data, mistakes in the rest of the code, and operational problems.

Making code safer is simple

The point of defensive coding is to make the code safer and to help whoever is going to maintain and support the code – not make their job harder. Defensive code is code – all code has bugs, and, because defensive code is dealing with exceptions, it is especially hard to test and to be sure that it will work when it has to. Understanding what conditions to check for and how much defensive coding is needed takes experience, working with code in production and seeing what can go wrong in the real world.

A lot of the work involved in designing and building secure, resilient systems is technically difficult or expensive. Defensive programming is neither – like defensive driving, it’s something that everyone can understand and do. It requires discipline and awareness and attention to detail, but it’s something that we all need to do if we want to make the world safe.

AppSec at RSA 2012 Conference

I spent last week at the RSA conference in San Francisco - my first time at a global IT security event like this. A lot to learn and think about, and a lot of problems to be solved.

Read my latest post at the SANS AppSec Street Fighter blog on my experience at this year's RSA conference.

Agile development teams CAN build secure software

We know that many development teams, especially small teams following Agile development practices, do a poor job of developing secure software. But is it Agile development specifically that is the problem? Many application security experts, especially those working in or for enterprises, think it is. I think they are wrong.

Read my latest post at the SANS AppSec Street Fighter blog on how Agile development teams CAN build secure software.

Technical Debt - How much is it Really Costing you?

The idea behind the technical debt metaphor is that there is a cost to taking short cuts (intentional technical debt) or making mistakes (unintentional technical debt) and that the cost of not dealing with these short cuts and mistakes will increase over time.

The problem with this metaphor is that with financial debt, we know how much it would cost to pay off a debt off today and we can calculate how much interest we will have to pay in the future. Technical debt though is much fuzzier. We don’t really know how much debt we have taken on – you may have taken on a lot of unintentional technical debt – and you may still be taking it on without knowing it. And we can’t quantify how much it is really costing us – how much interest we have paid so far, what the total cost may be in the future if we don’t take care of it today.

Some people have tried to put technical debt into concrete financial terms. For example, according to CAST Software’s CRASH report

“applications carry on average $3.61 of technical debt per line of code”.

For some reason, the average cost of Java apps was even higher: $5.42 per line of code. These numbers are calculated from running static structural analysis on their customers’ code.

Sonar, an Open Source dashboard for managing code quality, also tries to calculate a technical debt cost for a code base, again using static analysis findings like code coverage of automated tests, code complexity, duplication, violations of coding practices, comment density.

Thinking of technical debt in this way is interesting, but let’s stop pretending that these are hard numbers that we can use to make trade-off decisions. Although the numbers appear precise, they’re arbitrary, guesses. And they assume that technical debt can be calculated by a tool looking at the structure of the code. Unfortunately, dealing with technical debt is not that straightforward.

But if debt is too fuzzy to be measured in detailed cost terms, how do you know what kind of debt is hurting you the most, how do you know when you have too much? Let’s look at different kinds of technical debt, and how much they might cost you, using a fuzzier approach.

$$$ Making a fundamental mistake in architecture or the platform technology – you don’t find out until too late, until you have real customers using the system, that a key piece of technology like the database or messaging fabric doesn’t scale or isn’t reliable, or that you can’t scale out your architecture like you need to because of core dependency problems, or you you made some fundamentally incorrect assumptions on how the system is supposed to work or how customers will use it. Now you have no choice but to start again or at least rewrite big chunks of the system to get it to work or to keep it working, and you don’t have the time to do this properly.

$$-$$$ Error-prone code – the 20% of the code where 80% of bugs are found. Capers Jones says that all big systems have a small number of routines where bugs and problems cluster, code that is hard to understand and expensive and dangerous to change because it was done badly in the first place or it went to hell over time because of the accumulation of short-sighted fixes and changes. Not rewriting this code is one of the most expensive mistakes that developers make.

$-$$ The system can’t be easily tested – because you don’t have good automated tests, or the tests are brittle and slow and keep falling apart when you change the code. Testing costs can make up more than half of the cost of making any change or fix – sometimes testing can take much more time and cost much more than making the fix – and testing costs tend to go up over time as you write more code, as the system adds more interfaces and options.

$-$$ Not taking care of packaging and release and deployment. Relying too much on manual steps and manual checks, leading to mistakes and problems in production, late nights. Like testing, release and deployment costs don’t go away, they just keep adding up incrementally.

$-$$ Code that mysteriously works, but nobody is sure how or why – usually performance-critical or safety-critical low-level plumbing code written by a wizard who has long since left the company. It might be beautiful code, but if nobody on the team understands it, it’s a time bomb – someday, somebody is going to have to change it or fix it, or try to.

$-$$ Forward and backward compatibility adapters and compromises. This is necessary, short-term debt. But the cost rises the longer that you have to maintain these compromises.

$-$$ Out of date libraries and middleware stack – you’ve fallen behind on applying patches and upgrades. Even if the code that you have now is stable, you run some risk of unpatched security vulnerabilities. The longer that this goes on, the further behind you are, the higher the risk – at some point if the software is no longer supported or supportable, and your hand is called.

$-$$ Duplicate, copy-and-paste code. This is one of the bugaboos of technical debt and static analysis tools. Almost everybody has it. But how bad is it, really? The cost depends on how many clones developers have made, how often they need to be changed, how many subtle differences there are between the different copies, and how easily you can find the copies and keep track of them. If the developer who made the copies is still on the team and does a good job of keeping track of all of them, it doesn't cost much if anything.

$-$$ Known, outstanding bugs in code and unresolved static analysis findings. The cost and risk depends on how many bugs and warnings you have, and how nasty they are. But if they are real problems, they should have been fixed by now. Is a bug really a bug if it isn't bugging anyone?

$-$$ Inefficient design or implementation, “throwing hardware at it”, using too much memory or network bandwidth or CPU. Hardware is cheap, but these costs can add up a lot as you scale out.

$ Inconsistent use of programming idioms and patterns – developers either didn’t understand the existing patterns, or didn’t like them and introduced new ones, or didn’t care and just wanted to get their change done. It's ugly, and it can be frustrating for developers. But the real cost of living with the situation is often less than trying to clean it all up.

$ Missing or poor error handling and exception handling. It will constantly bite you in the ass in production, but it won’t cost a lot to at least get it mostly right.

$0.01 Hard coding, magic numbers, code that isn’t standards compliant, poor element naming, missing comments, and code that needs tidying. This is a pain in the ass, but it’s the kind of thing that is easy to clean up as part of standard refactoring work.

$0.01 Out of date documentation – another issue that is commonly considered in technical debt. But let’s be honest, most programmers don’t read documentation anyways. If nobody is using it, get rid of it. If people are using it, why isn’t it up to date?

$0.00 Hand-rolled code that could have and should have been done using built-in language features or libraries, or existing framework or common services. It’s disappointing when somebody recognizes it, but unless this hand-rolled code has lots of bugs in it, it’s a sunk cost, not a cost that is increasing over time.

There are different kinds of debt, with different costs. Figuring out where your real costs are, and what to do about it, isn't easy.

Agile’s Customer Problem

Agile methods like Scrum and XP both rely on a close and collaborative relationship and continual interaction with the customer – the people who are paying for the software and who are going to use the system. Rather than writing and reviewing detailed specifications and working through sign-offs and committees, the team works with someone who represents the interests of the customer to define business features and to decide what work needs to be done and when. One of the key problems in adopting these approaches is finding the right person to play the important role of the Customer (XP) or Product Owner (Scrum). I’ll use both terms interchangeably.

The team reviews their work with the Customer, the Customer answers any questions that they have and sets the team’s direction and priorities and makes sure that the team is focused on what is important to the business, owns and manages the requirements backlog, writes acceptance tests and decides when the software really is done.

A team’s success depends a lot on how good the Customer or Product Owner is, their knowledge and experience, their commitment to the project, how they make decisions and how good these decisions are. Mike Cohn in his book Succeeding with Agile explains that Product Owners have to be:

• committed to the team and available to answer questions, get the information that the team needs when the team needs it;


• an expert in the business: not only do they have to understand the domain, but they also need to understand what’s important strategically to the business and what’s important to the user community;


• a good communicator within the team and outside of the team;


• a good negotiator so that they can balance the needs of the team and the needs of different stakeholders;


• empowered – they must be able to make decisions on behalf of the customer – and they need to be willing to make tradeoffs and tough decisions when they have to.

An effective Product Owner also needs to be detail-oriented so that they can understand and resolve fine details of functionality and write functional acceptance tests. They should understand the basics of software development, at least the boundaries of what is and is not possible, what is hard to do and what isn’t and why so that they can appreciate technical dependencies and technical risks. They need to at least understand the rules of Scrum or XP – what’s expected of them, and how to play the game.

And they should understand the basics of project management and risk management. Because in the end, the Product Owner is the person who decides what gets done and what doesn’t.

The Product Owner is a product manager, project manager and business analyst all rolled into one.

Oh, and… they also need to be “collaborative by choice”, “agile in all things”, and…” fun and reasonable”.

Being in Two places at Once

The Product Owner has to work closely with the team, in touch with what the team is working on, to make sure that they can keep moving forward. But the Product Owner also has to stay involved in the business to understand what is going on and what is important. They have to be both inward-facing (working with the team, planning, holding reviews, attending meetings, prioritizing, helping to manage the backlog, defining requirements, answering questions and clarifying information) and outward-facing (working with the project’s sponsors and with the users of the system, making sure that the business understands what is happening in the project, making sure that they understand business priorities and competitive positioning and business trends and when any of this changes and how this could affect the project). They have to be in two places at once, which is physically impossible if the development team and the business aren’t co-located.

The Product Owner and Politics

There are political risks in the team’s relationship with the Product Owner, and in the Product Owner’s position and influence within their own organization. The Product Owner has to play politics with the team and inside the business, trying to promote the interests of the team and the project, reconciling conflicts between different stakeholders, trying to get and keep stakeholders onside, building coalitions. Their success, and the project’s success, depends on their ability to negotiate these issues.

Scrum assumes that the Product Owner is not only committed and talented and in touch with the business’s strategic priorities and with the concerns and needs of front line workers; but it also assumes that the Product Owner will always put the interests of the business ahead of their own interests. But a good Product Owner is usually an ambitious Product Owner – they are interested in the project as an opportunity to advance their career. Projects effect change, and with every change there are winners and losers. There is the real risk that the Product Owner’s success may put them in conflict with other important stakeholders in the business – by focusing on making their Product Owner happy, the team may be making enemies somewhere else in the organization without knowing it.

A Customer, or Many Customers?

Not only does the Product Owner decide what is important and what is going to get done, they are responsible for the project’s budget, and they are accountable for the project’s success. According to Ken Schwaber’s Agile Software Development with Scrum (the original definition of Scrum) the Product Owner is “the person who is officially responsible for the project”, “the one throat to choke” if the project fails or goes off the rails.

Expecting one person to take on all of this responsibility and this much work is unrealistic. It’s too much responsibility, too much risk, and too much work. For many Product Owners, it’s more than a full-time job – a direct contradiction to Agile values that put people first and emphasize realistic working hours and sustainable pace for the team.

This has been a problem since the beginning of Agile: a few months after the launch of the initial phase of C3 (the first XP project), the Customer representative quit due to burnout and stress, and could not be replaced, and the project was eventually cancelled.

Scrum still demands that the Product Owner role must be played by one person.

This doesn’t make sense, given that another fundamental underlying Agile principle is that people working together collaboratively make better decisions than one person working alone (“the Wisdom of Crowds”). If true, then why are the critical decisions about prioritization and direction and vision for the project made by one person?

The people behind XP eventually recognized that the simple idea of a Customer demanded too much from one person, that the workload and responsibility need to be shared by a Customer team. A Customer team means that you get the advantage of multiple perspectives, people with different specialties and experiences, and you have more help with answering questions and making decisions. It’s more sustainable and practical.

But this comes with its own set of problems:

• The development team has to reconcile differences in abilities, differences in understanding, different priorities, biases and political conflicts and personal conflicts within the Customer team.


• More time has to be spent explicitly keeping the Customer team itself in synch. There are more chances of mistakes and misunderstandings and dropped balls.


• Somebody still has to be in charge, make the important decisions – what Mike Cohn calls “the-buck-stops-here” person. The development team has to know that Customer decisions will not be over-ridden within the Customer team so that they can commit to getting work done.

The Customer in Maintenance

Maintenance and enhancement, where most of us will spend a lot of our careers, shows other problems with the Product Owner idea. First, it’s hard enough to get someone with the talent and drive and selflessness to represent the customer on a high-profile, strategic development project. It’s much harder to get anything close to this same level of commitment and talent for smaller projects, or for ongoing maintenance work. People with this knowledge and ability are likely to be running or supporting some important part of the business, not answering questions and helping to prioritize issues for your maintenance team.

And if you are lucky enough to find someone good, it’s hard to keep them – unlike a project, maintenance work doesn’t have a clear end date, so the team will need to get used to working with different Customers at different times, with different working styles, different agendas and different strengths.

The Product Owner is supposed to act as the single voice for the customer. But for a production system you are more likely to have too many voices, too many different people with different priorities and demands, all working for different parts of the business, all talking to different people on your team, trying to get what they need or want done. Or for some old legacy systems you may run into the opposite problem – nobody wants to take ownership of the system or its data, nobody knows enough or wants to be responsible for making business decisions.

Be your own Customer

All of these challenges don’t mean that you can’t work with the Product Owner model – obviously lots of teams are following Scrum and XP in one way or another. But you need to recognize the limitations and risks of this approach, and be prepared to fill in.

For example, many development and maintenance teams who work in a different location and especially a different timezone from the business fall back on a Customer Proxy: a business analyst or somebody else on the team who can help fill part of the Customer role, and work with people in the business to help answer questions and confirm requirements and priorities. It’s not as efficient as working directly with the business, but sometimes there isn’t a choice.

Even strong Customers need help at times. Many ScrumMasters and Product Owners find ways to split up the responsibilities more equitably and help each other out.

The Scrum Master or team lead or senior developers or senior testers, whoever is playing a technical leadership role on the team may have to step in and help fill in when the Customer isn’t available, when they are over-worked and can’t keep up, when they don’t understand, when they aren’t qualified to make a decision, or when they don’t care. To reconcile technical and business requirements and the needs of different stakeholders, and make technical and business trade-offs and long-term and short-term trade-offs. To communicate with business stakeholders. To follow-up on outstanding issues and unanswered questions and try to get answers from someone in the business. To write the acceptance tests for the customer – a common problem on Agile teams is that nobody on the business side is willing to help write acceptance tests, but they are willing to jump on the team when something is done wrong.

Be prepared to make more mistakes working this way – you will have to work with imperfect and incomplete information, sometimes you’ll have to make a best guess and go with it, and you will get requirements and priorities wrong. Test everything that you can, and get the product out to the business as quickly and as often as you can, and be prepared for negative feedback. You may not be able to build as close a relationship with the business as you could with a strong and committed Customer.

It’s a compromise, but it’s a necessary compromise that many teams have no choice but to make. As Mike Cohn points out in The Fallacy of One Throat to Choke, in the end it’s the team that fails, not just the Customer.

Source Code is an Asset, Not a Liability

Some people have tried to argue that source code is a liability, not an asset. Apparently this “is now widely accepted”
and

“this is a very strong idea that has a lot of impact across the IT industry and in the way developers view and perform their day-to-day work”.

Really?

The argument, as far as I can follow it, is that while engineers are paid to help design and build bridges and power plants, as developers we’re paid to “deliver business value”, and…

“Source code is merely the necessary evil that’s required to create value”

Source code, the software that we create, is only a means to and end. The software itself has no value, or worse it has negative value, because it creates a drag on your ability to innovate and move forward. The more code that you have, the higher your maintenance costs will be, therefore…

“… the best code of all is the code that's never written.”

Michael Feathers, who has a lot of smart things to say about source code, joined in on this discussion. In The Carrying-Cost of Code he says that

“code is inventory. It is stuff lying around and it has substantial cost of ownership. It might do us good to consider what we can do to minimize it.”

He goes so far as to suggest a goofy thought experiment where “every line of code written disappears exactly three months after it is written”. The point of this would be to get developers and the business to understand that the “costs of carrying code are real, but no one accounts for them”.

Feathers reinforces the valid points about the drag that unmaintained or poorly maintained legacy code has on companies. Writing less code to solve a problem is a good thing – it’s (usually) more efficient and (usually) costs less to maintain a smaller code base. And yes there is a necessary cost to maintaining software and working with existing software and changing it.

But none of this changes the fact that software is an asset

If you build and operate a power plant or a bridge, you have to maintain it – just like software. And like a bridge or a power plant, a newer, more modern, better-designed, more efficient and simpler asset is better than a big, old, complicated, expensive-to-maintain one.

The “software is a liability” argument seems to be that it’s not the software that’s the asset, it’s the “features and options” – the capabilities that the software provides. This is like saying that it’s not the power plant (which a company spent millions of dollars to design and engineer) that’s a valuable asset to a company, it’s the energy that it generates. It’s not the bridge – it’s the ability to drive over water. It’s not the airplane, it’s the ability to fly.

Pretending that software has no value in itself is silly. Try explaining this to accountants (don’t depreciate the airplane, depreciate the ability to fly!) and IP lawyers and to investors who buy software companies for their IP. They all understand that software and the ideas embodied in it are valuable and need to be treated as assets. The ideas themselves are only worth so much, even if they’re patented. But the ideas realized in software, actualized and proven and ready to be used or (better) already being used – that’s where the real value is. And this is the value that needs to be maintained and preserved.

Software is more valuable than other assets

The important difference between software and other assets is that software is much more plastic than other engineering work. Software is “soft” – it can be changed easily and inexpensively and quickly. This makes software more strategically valuable than “hard” assets like a building because software can be continuously adapted and renewed in response to changing situations, and transformed to create new business opportunities.

Software has to be changed to stay useful. The problem is NOT that we HAVE TO maintain software and change it to do things that it was never intended to do, to work in ways that it was never designed to, to do things that we couldn’t imagine a few years ago. This is the opportunity that software gives us – that we CAN do this. This is why Software is Eating the World.