Chronic and Acute Problems in Software

Chronic and Acute Problems in Software

Kevin Buchanan

October 04, 2016


Identifying the Illness

Medicine clearly distinguishes between chronic and acute conditions. Acute conditions have a clear and relatively sudden onset, affect a clearly defined component of the system, and respond quickly to treatment. Chronic conditions develop gradually, affect multiple parts of the system, and respond uncertainly to treatment.

Due to these differences, treatment for acute conditions differs greatly from treatment for chronic conditions in methods and efficacy. Chronic illness is the leading cause of death and disability in the US, while medicine has drastically improved at treating acute conditions. The current success rate for heart bypass surgery is 95 to 98 percent. Meanwhile, chronic heart disease remains the leading cause of death in the US. Medicine is good at treating acute conditions, but due to the more complex nature of chronic conditions, has not been able to achieve the same level of success with treating chronic conditions.

Compare this with certain kinds of problems we try to solve as developers, project managers, and business owners in software projects:

  • There's a bug in the ordering system, so we track it down and fix it.
  • The business needs a new feature added, so we write a story, and add the feature.
  • Our response time has degraded, so we do some diagnostics, rewrite a few things, and speed it up.
  • Our developers have decided that we'd be more productive writing our app in React, rather than Angular, so we rewrite it in React.

These are all acute problems. There are clear pain points, symptoms, and solutions for these problems. We like solving these problems because there's a sense of urgency, a knowable place to start, and quick feedback once we've destroyed the sickness. And we're good at solving these problems.

But there are other kinds of problems we encounter in software that lack these characteristics:

  • We've overrun many deadlines, so we try to estimate better, and write clearer stories, but we still frequently miss our estimates.
  • We chose to rewrite our app, but the development team failed to finish in time, now we're scrapping the project.
  • The last project failed, so now the business doesn't trust the development team, and now developers feel like they're being micromanaged.

These are harder problems to solve. How do we become productive again? How do we restore the business's trust in the engineering team? How do we ensure business owners are clearly communicating feature requirements to developers? These problems are more akin to chronic conditions, affecting multiple parts of our team in different ways, with an uncertain root cause, and developing gradually.

A Systems Perspective

We can't treat these problems the same way we pursued our acute problems. Rewriting our Ruby app in Go is not going to prevent the dev team from ever missing another deadline. Switching from incremental to fibonacci estimates isn't going to make you and the business argue about estimates less frequently. Using Jira instead of Pivotal Tracker isn't going to better prioritize your work. Asking your Ops team to be faster is not going to get you that database sooner. Asking the business owners to prioritize giving you information on how to implement the next feature over planning and rehashing the entirety of the project isn't going to get the project done sooner.

These kinds of chronic problems stem from conditions that exist across the system and manifest themselves in specific ways, some of which we end up seeing firsthand. To solve chronic problems on our software teams, we need to approach these problems from a systems perspective.

As Donella H. Meadows explains in "Thinking in Systems," a system is "an interconnected set of elements that is coherently organized in a way that achieves something." The connections and flows in a system manifest as stocks. Stocks are things that we can see, feel, and measure. On our teams we have stocks of things like knowledge, technical debt, pressure, money, time, happiness, and power at different places throughout our organization. Changes to one stock create changes in other stocks through various feedback loops that act based on a set of decisions, rules, or physical laws.

For example, you have a feedback loop that assesses your job satisfaction. The flows of both pay and workload into your stocks of money and stress initiate this loop. When your stress stock changes, the feedback loop involves you reassessing your job satisfaction. You might meet with your employer to discuss changing the flow of pay to your money stock or workload affecting your stress stock.


Of course, systems are more complex than we can model in one diagram. This is a simplification of the system focusing only on the two things we're discussing in this scenario, as any diagram of any system would be a simplification. The important part of a system diagram like this is to identify the components of our system relevant to the problem we are trying to solve.

Human Software Systems

Once we start identifying the flows, stocks, and feedback loops of the system facing a chronic problem, we can then figure out where to add, remove, or modify certain feedback loops to bring our stores into proper levels over time.

For example, if we need to build more knowledge of business requirements in our dev team's store of knowledge, we need to build up the feedback loop that asks for more information and adjusts that flow. That probably sounds simpler than it is though, because it requires effort from multiple parts of our team—developers taking the time to ask questions and business owners being available and taking the time to answer questions (and vice-versa). It may involve adding new people to the team to facilitate this loop, or maybe moving others to new roles if their current position inhibits the feedback loop.

Problems of trust—territorialism; animosity between the dev team, project managers, and business owners; aversion to change; excessive planning and process—often arise due to a system condition that Meadows calls "seeking the wrong goal." In these systems, "behavior is particularly sensitive to the goals of feedback loops. If the goals—the indicators of satisfaction of the rules—are defined inaccurately or incompletely, the system may obediently work to produce a result that is not really intended or wanted."


In this case, developers may have a feedback loop that tells them to continue writing code, but no feedback loop exists to tell them whether the code they're writing is buggy or whether people actually like what they're building. Managers may have a feedback loop that determines whether they've had the right meetings, but no feedback loop that tells them what pain points their reports would actually like to discuss. Business owners may have a feedback loop that tells them whether they've outlined everything they think the developers should know about their needs, but no feedback loop that adequately tells them what the developers really need to know right now.


In this system, everyone has a feedback loop that seeks to satisfy their own goals and the system ends up slowly producing some software that works, but there are lots of bugs and nobody is particularly happy. The system is missing feedback loops that address the real welfare of the system—that of creating high-quality software that the business loves to use. XP, Agile, Kanban, all strive to explicitly drive out these important feedback loops and turn them into habits. What you call the process doesn't actually matter once you can clearly say that your process provides the necessary feedback loops for your system to seek the right goal.

Changing Habit

The hardest part of fixing any of these chronic, system-wide problems is that they involve changing people's habits. Changing habit is hard, specifically because the entire point of a habit is to allow us to reduce the amount of effort we put forward in doing certain things:

When a habit emerges, the brain stops fully participating in decision making. It stops working so hard, or diverts focus to other tasks. So unless you deliberately fight a habit—unless you find new routines—the pattern will unfold automatically.

— Charles Duhigg, The Power of Habit

In fact, you could say that organization-wide problems in software resemble chronic illness because they are brought about by habit, which itself resembles a chronic illness. Habit, too, develops gradually, is long-lasting, and responds uncertainly to treatment. But, we know we can change our habits. It just takes time and awareness of the habit and the conditions that led to it.

Software developers and participants in the software development process love to solve problems. But we can't approach every problem the same way. Our biggest failure in solving certain chronic problems would be to look for clear root causes and expect quick results once our clear-cut solution is implemented—as if we can solve these complex problems the same way we've solved acute problems on our teams. By acknowledging the complexity of chronic problems, we can give ourselves and our team members a more accurate assessment of the multi-faceted, long-term, uncertain, and habit-changing approach that we need to take to solve the problem. That way we can approach the problem with the tact and patience it deserves while reducing discouragement and frustration for ourselves and our team members when our simple solutions don't fix things right away.


Kevin Buchanan

Principal Crafter

Kevin Buchanan writes code and rides bikes in California. He is an experienced software professional who has led development teams to deliver complex systems that solve unique business challenges — like pharmaceuticals, telemedicine, medical devices, insurance, private equity, and more.