The Agile Executive

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Posts Tagged ‘Development on Margin

How to Break the Vicious Cycle of Technical Debt

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The dire consequences of the pressure to quickly deliver more functions and features to the market have been described in detail in various posts in this blog (see, for example, Toxic Code). Relentless pressure forces the development team to take technical debt. The very same pressure stands in the way of paying back the debt in a timely manner. The accrued technical debt reduces the velocity of the development team. Reduced development velocity leads to increased pressure to deliver, which leads to taking additional technical debt, which… It is a vicious cycle that is extremely difficult to break.

Figure 1: The Vicious Cycle of Technical Debt

The post Using Credit Limits to Constrain “Development on Margin” proposed a way of coping with the vicious cycle of technical debt – placing a limit on the amount of technical debt a development team is allowed to accrue. Such a limit addresses the demand side of the software development process. Once a team reaches the pre-determined technical debt limit (such as $3 per line of code) it cannot continue piling on new functions and features. It must attend to reducing the technical debt.

A complementary measure can be applied to the supply side of the software development process. For example, one can dynamically augment the team by drawing upon on-demand testing. uTest‘s recent announcement about securing Series C financing explains the rationale for the on-demand paradigm:

“The whole ‘appification’ of software platforms, whether it’s for social platforms like Facebook or mobile platforms like the iPhone or Android or Palm, or even just Web apps, creates a dramatically more complex user-testing matrix for software publishers, which could mean media companies, retailers, enterprise software companies,” says Wienbar. “Anybody who has to interact with consumers needs a service to help with that testing. You can’t cover that whole matrix with your in-house test team.”

Likewise, on-demand development can augment the development team whenever the capacity of the in-house team is insufficient to satisfy demand. IMHO it is only a matter of little time till marketplaces for on-demand development will evolve. All the necessary ‘ingredients’ for so doing – Agile, Cloud, Mobile and Social – are readily available. It is merely a matter of putting them together to offer on-demand development as a commercial service.

Whether you do on-demand testing, on-demand development or both, you will soon be able to address the supply side of software development in a flexible and cost-effective manner. Between curtailing demand through technical debt limits and expanding supply through on-demand testing/development, you will be better able to cope with the relentless pressure to deliver more and quicker than the capacity of your team allows.


Can Technical Debt Constitute a Breach of Implied Warranties?

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POGO_film_diffs by Dancing Lemur.

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.

How to Combine Development Productivity Data with Software Quality Metrics

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Consider the situation described in Should You Invest in This Software:

  • One of your portfolio companies expects to ship 500K lines of code in 6 months.
  • The company asks for additional $2M to complete development and bring the product to market.
  • Using technical debt quantification techniques you find the technical debt amounts to $1M.

You are not at all comfortable “paying back” the technical debt in addition to funding the requested $2M. You wonder whether you should start afresh instead of trying to complete and fix the code.

Photo credit: @muntz (Flickr)

A good starting point for assessing the fresh start option is Michael Mah‘s studies of software productivity. Based on the QSMA SLIM metrics database of more than 8,000 projects, Michael will probably bracket the productivity per person in a team consisting of product management, development and test at 10-15K lines of code per year. If you use the 15K lines of code per year figure for the purposes of the analysis, 500K lines of code could theoretically be delivered with an investment of about 33.3 (500/15) man years. Assuming average loaded cost of $99,000 per man-year,  the software represents a programming effort of $3.3M. Not much is left if you deduct $3M ($2M+1M) from $3.3M…

Five considerations are of paramount importance in evaluating the start afresh option:

  • The comparison above ($3.3M versus $3.0M) is timeless. It is a snapshot at a certain point in time which does not take into account the value of time. To factor in the time dimension, the analysis needs to get into value (as distinct from cost) considerations. See the note on Intrinsic Quality v. Extrinsic Quality at the bottom of this post.
  • Your “mileage” may vary. For example, best in class teams in large software projects have reported productivity of 20K lines of code per team member per year. As another example, productivity in business applications is very different from productivity in real-time software.
  • If you decide to start with a brand new team, remember Napoleon’s quip: “Soldiers have to eat soup together for a long time before they are ready to fight.”
  • If you decide to start afresh with the same team plus some enhancements to the headcount, be mindful of  ‘Mythical Man-Month‘ effects. Michael Mah’s studies of the BMC BPM projects indicate that such effects might not hold for proficient Agile teams. Hence, you might opt to go Agile if you plan to enhanced the team in an aggressive manner.
  • Starting afresh is not an antidote to accruing technical debt (yet again…) over time. But, it gives you the opportunity to aggressively curtail technical debt by applying the techniques described in Using Credit Limits to Constrain Development on Margin. For example, you might run source code analytics every two weeks and go over the results in the bi-weekly demo.

As long as you are mindful of these five aspects (timeless analysis, your mileage may vary, Napoleon’s quip, mythical man-month effects and credit limits on technical debt), combining technical debt figures with productivity data is an effective way to consider the pros and cons of “fix it” versus starting afresh. The combination of the two simplifies a complex  investment decision by reducing all considerations to a single common denominator – $$.

Note: This is not a discussion from a value perspective. The software, warts and everything, might (or might not)  be valuable to the target customers. The reader is referred to Jim Highsmith‘s analysis of Intrinsic quality versus Extrinsic Quality in Agile Project Management: Creating Innovative Products. See the Cutter Blog post entitled Beyond Scope, Schedule and Cost: Measuring Agile Performance for a short summary of the distinction between the two.

Using Credit Limits to Constrain “Development on Margin”

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Buying (stocks) on margin is broadly recognized as a risky investment strategy. Funding long-term investments with short-term debt exposes the investor to margin calls as he/she might not be able to secure more financing when needed. The resultant margin call is never pleasant.

The accrual of technical debt in the course of aggressively developing functions and features is quite a similar phenomenon. The CTO is betting the functionality he/she is developing will pay off before the need to “pay back” the technical debt becomes imperative. The temptation to do so is particularly strong due to the lack of credit limits on technical debt. For all practical purposes the CTO is “developing on margin.”

In his comprehensive studies of the economics of software, Capers Jones has actually put a 3-5 year ceiling on the economical viability of developing on margin:

Indeed, the economic value of lagging applications is questionable after about three to five years. The degradation of initial structure and the increasing difficulty of making updates without “bad fixes” tends towards negative returns on investment (ROI) within a few years.

As the CEO leading a company, or the venture capitalist funding it, you can restrain development on margin by establishing credit limits. Use a combination of static code analysis with dynamic program analysis to calculate the amount of accrued technical debt in $$ terms. (An illustration of such calculation as well as a breakdown of the technical debt is given in the Sonar chart above). Set a limit (say $0.25 per line of code) on the amount of permitted technical debt. Once the limit is reached, developers are not allowed to continue developing new functionality – they have to first reduce (and hopefully eliminate) their technical debt.

A very simple “Lacmus test” is available to the CEO/VC until the code is instrumented and the analytics illustrated above generated. Ask your CTO about unit test coverage. If the coverage is low (say <30%), chances are the technical debt is high. Whether the CTO realizes it or not, low unit test coverage is a good indicator of technical debt of all kinds. Moreover, the investment required to develop a full-fledged suite of unit tests is often the largest component of the technical debt to be paid back.