Jim Bird

About Jim Bird

Jim is an experienced CTO, software development manager and project manager, who has worked on high-performance, high-reliability mission-critical systems for many years, as well as building software development tools. His current interests include scaling Lean and Agile software development methodologies, software security and software assurance.

The Real Cost of Change in Software Development

There are two widely opposed (and often misunderstood) positions on how expensive it can be to change or fix software once it has been designed, coded, tested and implemented. One holds that it is extremely expensive to leave changes until late, that the cost of change rises exponentially. The other position is that changes should be left as late as possible, because the cost of changing software is – or at least can be – essentially flat (that’s why we call it “soft”ware).

Which position is right? Why should we care? And what can we do about it?

Exponential Cost of Change

Back in the early 1980s, Barry Boehm published some statistics (Software Engineering Economics, 1981) which showed that the cost of making a software change or fix increases significantly over time – you can see the original curve that he published here.

Boehm looked at data collected from Waterfall-based projects at TRW and IBM in the 1970s, and found that the cost of making a change increases as you move from the stages of requirements analysis to architecture, design, coding, testing and deployment. A requirements mistake found and corrected while you are still defining the requirements costs almost nothing. But if you wait until after you’ve finished designing, coding and testing the system and delivering it to the customer, it can cost up to 100x as much.

A few caveats here. First, the cost curve is much higher in large projects (in smaller projects, the cost curve is more like 1:4 instead of 1:100). Those cases where the cost of change rises up to 100x are rare, what Boehm calls Architecture-Breakers, where the team gets a fundamental architectural assumption wrong (scaling, performance, reliability) and doesn’t find out until after customers are already using the system and running into serious operational problems. And this analysis was all done on a small data sample from more than 30 years ago, when developing code was much more expensive and time-consuming and paperworky, and the tools sucked.

A few other studies have been done since then which mostly back up Boehm’s findings – at least the basic idea that the longer it takes for you to find out that you made a mistake, the more expensive it is to correct it. These studies have been widely referenced in books like Steve McConnell’s Code Complete, and used to justify the importance of early reviews and testing:


Studies over the last 25 years have proven conclusively that it pays to do things right the first time. Unnecessary changes are expensive. Researchers at Hewlett-Packard, IBM, Hughes Aircraft, TRW, and other organizations have found that purging an error by the beginning of construction allows rework to be done 10 to 100 times less expensively than when it’s done in the last part of the process, during system test or after release (Fagan 1976; Humphrey, Snyder, and Willis 1991; Leffingwell 1997; Willis et al. 1998; Grady 1999; Shull et al. 2002; Boehm and Turner 2004).

In general, the principle is to find an error as close as possible to the time at which it was introduced. The longer the defect stays in the software food chain, the more damage it causes further down the chain. Since requirements are done first, requirements defects have the potential to be in the system longer and to be more expensive. Defects inserted into the software upstream also tend to have broader effects than those inserted further downstream. That also makes early defects more expensive.

There’s some controversy over how accurate and complete this data is, how much we can rely on it, and how relevant it is today when we have much better development tools and many teams have moved from heavyweight sequential Waterfall development to lightweight iterative, incremental development approaches.

Flattening the Cost of Changing Code

The rules of the game should change with iterative and incremental development – because they have to.

Boehm realized back in the 1980s that we could catch more mistakes early (and therefore reduce the cost of development) if we think about risks upfront and design and build software in increments, using what he called the Spiral Model, rather than trying to define, design and build software in a Waterfall sequence.

The same ideas are behind more modern, lighter Agile development approaches. In Extreme Programming Explained (the 1st edition, but not the 2nd) Kent Beck states that minimizing the cost of change is one of the goals of Extreme Programming, and that a flattened change cost curve is “the technical premise of XP”:


Under certain circumstances, the exponential rise in the cost of changing software over time can be flattened. If we can flatten the curve, old assumptions about the best way to develop software no longer hold…

You would make big decisions as late in the process as possible, to defer the cost of making the decisions and to have the greatest possible chance that they would be right. You would only implement what you had to, in hopes that the needs you anticipate for tomorrow wouldn’t come true. You would introduce elements to the design only as they simplified existing code or made writing the next bit of code simpler.

It’s important to understand that Beck doesn’t say that with XP the change curve is flat. He says that these costs can be flattened if teams work towards this, leveraging key practices and principles in XP, such as:

  • Simple Design, doing the simplest thing that works, and deferring design decisions as late as possible (YAGNI), so that the design is easy to understand and easy to change
  • continuous, disciplined Refactoring to keep the code easy to understand and easy to change
  • Test-First Development – writing automated tests upfront to catch coding mistakes immediately, and to build up a testing safety net to catch mistakes in the future
  • developers collaborating closely and constantly with the Customer to confirm their understanding of what they need to build and working together in Pairs to design solutions and solve problems, and catch mistakes and misunderstandings early
  • relying on working software over documentation to minimize the amount of paperwork that needs to be done with each change (write code, not specs)
  • the team’s experience working incrementally and iteratively – the more that people work and think this way, the better they will get at it.

All of this makes sense and sounds right, although there are no studies that back up these assertions, which is why Beck dropped this change curve discussion from the second edition of his XP book. But by then the idea that change could be flat with Agile development had already become accepted by many people.

The importance of Feedback

Scott Amber agrees that the cost curve can be flattened in Agile development, not because of Simple Design, but because of the feedback loops which are fundamental to iterative, incremental development. Agile methods optimize feedback within the team, developers working closely together with each other and with the Customer and relying on continuous face-to-face communications. And following technical practices like Test-First Development and Pair Programming and Continuous Integration makes these feedback loops even tighter.

But what really matters is getting feedback from the people using the system – it’s only then that you know if you got it right or what you missed. The longer that it takes to design and build something and get feedback from real users, the more time and work that is required to get working software into a real customer’s hands, the higher your cost of change really is.

Optimizing and streamlining this feedback loop is what is driving the Lean Startup approach to development: defining a Minimum Viable Product (something that just barely does the job), getting it out to customers as quickly as you can, and then responding to user feedback through Continuous Deployment and A/B testing techniques until you find out what customers really want.

Even flat change can still be expensive

Even if you do everything to optimize these feedback loops and minimize your overheads, this still doesn’t mean that change will come cheap. Being fast isn’t good enough if you make too many mistakes along the way.

The Post Agilist uses the example of painting a house: assume that it costs $1,000 each time you paint the house, whether you paint it blue or red or white. The cost of change is flat. But if you have to paint it blue first, then red, then white before everyone is happy, you’re wasting time and money.


“No matter how expensive or change the ‘cost of change’ curve may be, the fewer changes that are made, the cheaper and faster the result will be…Planning is not a four letter word.” [however, I would like to point out that “plan” is].

Spending too much time upfront in planning and design is waste. But not spending enough time upfront to find out what you should be building and how you should be building it before you build it, and not taking the care to build it carefully, is also a waste.

Change gets more expensive over time

You also have to accept that the incremental cost of change will go up over the life of a system, especially once a system is being used. This is not just a technical debt problem. The more people using the system, the more people who might be impacted by the change if you get it wrong, the more careful you have to be, which means you need to spend more time on planning and communicating changes, building and testing a roll-back capability, and roll changes out slowly using Canary Releases and Dark Launching – which add costs and delays to getting feedback.

There are also more operational dependencies that you have to understand and take care of, and more data that you have to change or fix up, making changes even more difficult and expensive. If you do things right, keep a good team together and manage technical debt responsibly, these costs should rise gently over the life of a system – and if you don’t, that exponential change curve will kick in.

What is the Real Cost of Change?

Is the real cost of change exponential, or is it flat? The truth is somewhere in between.

There’s no reason that the cost of making a change to software has to be as high as it was 30 years ago. We can definitely do better today, with better tools and better, cheaper ways of developing software. The keys to minimizing the costs of change seem to be:

  1. Get your software into customer hands as quickly as you can. I am not convinced that any organization really needs to push out software changes 10-50-100x a day, but you don’t want to wait months or years for feedback either. Deliver less, but more often. And because you’re going to deliver more often, it makes sense to build a Continuous Delivery pipeline so that you can push changes out efficiently and with confidence. Use ideas from Lean Software Development and maybe Kanban to identify and eliminate waste and to minimize cycle time.
  2. We know that even with lots of upfront planning and design thinking, we won’t get everything right upfront – this is the Waterfall fallacy. But it’s also important not to waste time and money iterating when you don’t need to. Spending enough time upfront in understanding requirements and in design to get it at least mostly right the first time can save a lot later on.
  3. And whether you’re working incrementally and iteratively, or sequentially, it makes good sense to catch mistakes early when you can, whether you do this through Test First Development and pairing, or requirements workshops and code reviews, whatever works for you.

 

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One Response to "The Real Cost of Change in Software Development"

  1. Jenna says:

    Hi Jim, thanks for sharing your thoughts, you make a great point here:

    “..The longer the defect stays in the software food chain, the more damage it causes further down the chain..”

    This is a timely reminder for software developers to flush out the gremlins as quickly as possible and test, test and test again.

    Cheerio, Jenna

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