Posts Tagged ‘Technical Debt’
- TD(current build) > TD(previous build)
- TD per line of code > $1
- TD as % of cost > 50%
- Many others…
If you follow this recommendation while integrating continuously, chances are some fixing might need to be done multiple times a day. This being the case, the relationship between the daily stand-up meeting and the build fail event needs to be examined. If the team anyway re-groups whenever the build fails, what is the value add of the daily stand-up meeting?
Figure 1 below, taken from Agile Software Development with Scrum, examines the feedback loop of Scrum from a process control perspective. It shows the daily stand-up meeting providing the process control function.
Figure 1: Process Control View of Scrum
Figure 2 below shows the feedback loop being driven by the build process instead of by the daily stand-up meeting. From a process control standpoint it is essentially identical to Figure 1. Control through the regularly scheduled daily stand-up meeting has simply been replaced by event-driven control. The team re-groups whenever the build fails.
Figure 2: Build Driven Process Control
Some groups, e.g. the Scrum/Kanban teams under Erik Huddleston and Stephen Chin at Inovis have been practicing Event Driven Scrum for some time now. A lot more data is needed in order to validate the premise that Event Driven Scrum is indeed an improvement over vanilla Scrum. Until the necessary data is collected and examined, two intuitive observations are worth mentioning:
- Event Driven Scrum preserves the nature of Scrum as an empirical process.
- A control unit that is triggered by events (build fail) is in the very spirit of adaptive Agile methods.
“[The 100% loan-to-value subprime loan is] the most dangerous product in existence and there can be nothing more toxic…”
This quip by Angelo Mozilo, founder of Countrywide Financial, led me to coin the term “toxic code.” The code stops being an asset when it accumulates technical debt equal to the value it is expected to generate. As matter of fact, the code could become a liability when the cost to “pay back” the technical debt exceeds the revenues the code would generate. Such situations have been known to happen more often than we might realize. They occur, for example, when numerous customers develop elaborate business processes around poor quality enterprise software.
I will be exploring the topic in my May 27, 2010 presentation at The Path to Agility conference in Columbus, OH. Here is the abstract of my talk:
Technical debt had originally been conceived as an expediency measure – “a little debt speeds development so long as it is paid back promptly with a rewrite.” However, like financial debt, unrestrained borrowing can lead to a broad spectrum of difficulties, from collapsed roadmaps to inability to respond to customer problems in a timely manner, and anything in between.
Recent advances in source code analysis enable us to quantify technical debt and express it in $$ terms. By so doing, the software development process can be governed with unprecedented effectiveness. It is possible to constrain the “development on margin” mal-practice and avoid the toxic code phenomenon: technical-debt-to-value ratio of 100%. Moreover, even toxic code can ultimately be “marked-to-market” by reducing/eliminating technical debt.
The combination of Agile refactoring practices with technical debt analytics brings rigor to the software development process through:
- Providing quantifiable data about the overall state of the code and its value
- Highlighting error-prone modules in the code and offering guidance to fixing them in a biggest-bang for-the-buck manner
- Pinpointing technical issues all the way down to the individual line of code level
- Balancing the technical debt work stream vis-a-vis other work streams
In the course of managing software development in this manner, your team will improve its design, coding, testing and project management skills. Ultimately, these improvements are the best antidote against accrual of technical debt in the future.
Photo credit: Dancing Lemur (Flickr)
Cunningham’s quip “A little debt speeds development so long as it is paid back promptly with a rewrite” is intuitively very clear. We are talking about short-term debt which will be reduced, and hopefully eliminated in entirety, at the earliest possible time.
The question this post addresses is what happens when the expected short-term technical debt becomes a significant long-term debt? Specifically, can technical debt under some conditions constitute a breach of implied warranties?
In his InformIT article Don’t “Enron” Your Software Project, Aaron Erickson coined the term “Technical Fraud” and connected it to Lemmon Laws:
As a reaction to seeing this condition and its deleterious effects, I coined the term technical fraud to refer to the practice of incurring unmanaged and hidden technical debt. Many U.S. states have “lemon laws” that make it illegal to knowingly sell someone a car that has undisclosed maintenance problems. Selling a “lemon” is a fraudulent practice in the world of cars, and it should be considered as such in the world of software.
It is a little tricky (though not impossible – see Using Credit limits to Constrain Development on Margin) to define the precise point where technical debt becomes “unmanaged.” One needs to walk a fine line between technical/methodical incompetence and resource availability to determine technical fraud. For example, if your code has 35% coverage, is it or is not unmanaged? Does the answer to this question change if your cyclomatic complexity per class exceeds 30? I would think the courts might be divided for a very long time on the question when does hidden technical debt represent a fraudulent misrepresentation.
One component of technical debt deserves special attention in the context of this post. I am referring to the conscious decision not to do unit testing at all.
Best I understand it, the rationale for not “bothering” with unit testing is a variant of the old ploy “we do not have time for testing here.” It is a resource allocation strategy that bets on the code being miraculously bug-free. Some amount of functional testing is done out of necessity – the code in customers hands needs to function as proclaimed. But, the pieces of code from which functionality is constructed are not subject to direct rigorous testing. The individual units of code will be indirectly exercised in some manner through functional testing, but not in a systemic manner to verify and validate correctness of the units of code per se.
Such a conscious decision IMHO indicates no intention to pay back this category of technical debt – unit test coverage. It is therefore quite incompatible with the nature of an implied warranty:
An implied warranty is as an unstated promise, assumed by the law in most sales transactions, that the product will be of at least average quality and will do what the average customer would expect it to do [The Reader’s Digest Legal Questions & Answers Book]
To #1 defense open to a software vendor who gets sued over lack of unit testing is that a fair average quality of software can be attained without any unit testing. As a programmer, I would think such defense would fly at the teeth of the availability since 1987 of the IEEE Standard for Software Unit Testing.
It is fascinating to note the duality between contracts and programming. For the programmer who follows the tenets of design by contract, “a unit test provides a strict, written contract that the piece of code must satisfy…”
Disclaimer: I am not an expert in the law. The opinion expressed in this post merely represents my layman’s understanding of principles of contract law that might be applicable to technical debt situations.
Photo credit: tengtan (Flickr)
Most posts on technical debt in this blog emphasize the use of technical debt for strategic decision-making. In this post we will point out the use of technical debt in Agile teams at the tactical level. Specifically:
- Every two weeks; and/or,
- With every build.
Taking a close look at the various components of technical debt during the bi-weekly iteration review meeting provides plenty of useful information to the process. For example, you might look for insights to explain the following:
- Why is the unit test coverage figure going down?
- Any particular reason the cyclomatic complexity figure has gone up?
- Why is the figure of merit for design lower than the figure indicated in the previous iteration review meeting?
- Many others…
The emphasis in this mode of operation is on guiding the retrospection. Plenty of good and valid reasons might exist for any of the trends mentioned above. However, observing the trends helps you ask the right questions, focusing on what happened during the iteration just completed. In conjunction with technical debt data from previous iteration review meetings, trends that characterize your software development project become visible. You may or may not need to change anything you are doing, but you become very conscious of any “let’s not change” decision.
An intriguing practice suggested by colleague and friend Erik Huddleston is to make technical debt a criterion for the build to pass. The build automatically fails if the technical debt figure has gone up. Or, if you are very focused on a specific aspect of technical debt such as complexity, you fail the build whenever the complexity figure of merit rises above a certain pre-determined threshold. For example, you might fail a build in which the cyclomatic complexity per method has exceeded 4.
The power of failing a build whenever the technical debt arises is in utilizing the build as an exceptionally effective influence point. You instill the discipline of reducing technical debt one build at a time. If your team aggressively practices continuous integration, it will address technical debt issues multiple times a day. Instead of staring at a “mountain” of technical debt towards the release of a product, you chunk it to really small increments that get addressed “real-time.” For instance, a build that failed due to lack of comments can usually be fixed very quickly by the developer who “upset the apple cart” while the logic embedded in the code is fresh on his/her mind.
A good insight to the way the tactical use of technical debt techniques adds value is provided by the following observation: the technical debt data is observed from outside the Agile process. Hence, technical debt data is nicely suited to guiding the process. If you think of the software engineering fabric as a virtual stack, the technical debt “layer” could be considered a layer above the Agile process.
Technical debt is usually perceived as a measure of expediency. You borrow a little (time) with the intent of paying it back as soon as possible. To quote Ward Cunnigham:
Shipping first time code is like going into debt. A little debt speeds development so long as it is paid back promptly with a rewrite… I thought that rushing software out the door to get some experience with it was a good idea, but that of course, you would eventually go back and as you learned things about that software you would repay that loan by refactoring the program to reflect your experience as you acquired it.
As is often the case with financial debt, technical debt accrues with compound interest. Once it reaches a certain level (e.g. $1 per line of code) you stare at a difficult question:
Should I ship this code before reducing the accrued technical debt?!
The Figure below, taken from An Objective Measure of Code Quality by Mark Dixon, answers the question with respect to one important component of technical debt – cyclomatic complexity. Once complexity per source code file exceeds 74, the file is for most practical purposes guaranteed to contain errors. Some of the errors in such a file might be trivial. However, a 2007 study by Capers Jones indicates about a third of the errors found in released code are likely to be serious enough to stop an application from running or create erroneous outputs.
To answer the question cited above – Should You Ship This Software Before Reducing Technical Debt?! – examine both cost and risk for the number of error-prone files you are about to unleash:
- The economics of defect removal clearly favor early defect removal over late defect removal. The cost of removal grows exponentially as function of time.
- Brand risk should be first and foremost on your mind. If complexity figures higher than 74 per file are more of the norm than the exception, you are quite likely to tarnish your image due to poor quality.
If you decide to postpone the release date until the technical debt has been reduced, you can apply yourself to technical debt reduction in a biggest-bang-for-the-buck manner. The analysis of complexity can identify the hot spots in your code, giving you a de-facto roadmap you would be wise to follow.
Conversely, if you opt to ship the code without reducing technical debt, you might lose this degree of freedom to prioritize your “fix it” work. Customer situations and pressures might force you to attend to fixing modules that do not necessarily provide as much bang for the buck.
Postscript: Please note that the discussion in this post is strictly limited to intrinsic quality. It does not address at all extrinsic quality. In other words, reducing/eliminating technical debt does not guarantee that the customer will find the code valuable. I would suggest reading Beyond Scope, Schedule and Cost: Measuring Agile Performance in the Cutter Blog for a more detailed analysis of the distinction between the two.
Erratum: The figure above is actually taken from a blog post on the Mark Dixon paper cited in my post. See McCabe Cyclomatic Complexity: the proof is in the pudding. My apology for the error.
Source: 17th/21st Lancers c. 1922-1929 “THE FIGHTING SPIRIT!”
Agile consultants on a development project often start by helping the team construct a backlog. The task is sufficiently concrete to get all stakeholders (product management, project management, development, test, any others) on a collaborative track through the creation of a key artifact. The backlog establishes a base line for the tasks to be carried out in the project.
For a DevOps project, start by establishing the technical debt of the software to be released to operations. By so doing you build the foundations for collaboration between development and operations through shared data. In the DevOps context, the technical debt data form the basis for the creation and grooming of a unified backlog which includes various user stories from operations.
Apply the same approach when you are fortunate to be able to include folks from operations in the Agile team from the very beginning. You start with zero technical debt, but you track it on an ongoing basis and include the corresponding “fix-it” stories in the backlog as you accrue the debt. Running technical debt analytics on the source code every two weeks is a good practice to follow.
As the head of development, you might not be comfortable sharing technical debt data. This being the case, you are not ready for DevOps.