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Posts Tagged ‘The Performance Paradox

How Technical Debt Ties to Cloud, Mobile and Social

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http://en.wikipedia.org/wiki/File:West_Side_Highway_collapsed_at_14th.jpg

Many years ago, when I came to the US, I was shocked to the core seeing the collapsed West Side Highway in New York City. I simply could not believe that a highway would be neglected to that extent amidst all the affluence of the city. The contrast was too much for me.

Nowadays I often have a deja vu sensation in various technical debt engagements in which I find the code crumbling. This sensation is not so much about what happened (see The Real Cost of a One Trillion Dollars in IT Debt: Part II – The Performance Paradox for an explanation of the economics of the neglect of software maintenance during the past decade), but about the company for which I do the assessment giving up on immense forthcoming opportunities.

Whether you do or do not fully subscribe to the vision of the Internet-of-Things depicted in the figure below, it is fairly safe to assume that your business in the years to come will be much more connected to the outside world than it is now. The enhanced connectivity might come through mobile applications, through social networks or through the cloud. As a matter of fact, it is quite likely to come through a confluence of the three: Cloud, Mobile and Social.

http://en.wikipedia.org/wiki/File:Internet_of_Things.png

In the context of current trends in cloud, mobile and social, your legacy software is like the West Side Highway in New York City. If you maintain it to an acceptable level, it can become the core of two major benefits of much higher connectivity and connectedness in the not-too-far future:

  • Through mobile and social your legacy software will enable you to flexibly produce, market and distribute small quantities of whatever your products might need to be in niche markets.
  • Through cloud it will enable you to offer these very same products and many others as services.

Conversely, if you consistently neglect to pay back your technical debt, your legacy code is likely to collapse due to the effects of software decay. You certainly will not be able to get it to interoperate with mobile and social networking applications, let alone offer it in the form of cloud services. Nor would you be able to wrap additional services around decaying legacy code. Take a look at the warehousing and distribution services offered by Amazon to get a sense of what this kind of additional services could do for your core business: they will enable you to transform your current business design by adding an Online-to-Offline (O2O) component to it.

What is the fine line differentiating “acceptably maintained” code from toxic code? I don’t think I have conducted a large enough sample of technical debt assessments to provide a statistically significant answer. My hunch is that the magic ceiling for software development in the US is somewhere around $10 per line of code in technical debt. As long as you are under this ceiling you could still pay back your technical debt (or a significant portion of it) in an economically viable manner. Beyond $10 per line of code the decay might prove too high to fix.

Why $10 and not $1 or $100 per line of code? It is a matter of balancing investment versus debt. An average programmer (in the US) with a $100,000 salary would probably be able to produce about 10K lines of Java code per year. The cost of a line of code under these simplistic assumptions is $10. Something is terribly wrong if the technical debt exceeds the cost per line. They call it living on margin.

Action item: CIOs should conduct a technical debt assessment on a representative sample of their legacy code. A board level discussion on the strategic implications for the company is called for if technical debt per line of code exceeds $10. The board discussion should focus on the ability of the company (or lack thereof) to participate in the business tsunami that cloud, mobile and social are likely to unleash.

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The Real Cost of One Trillion Dollars in IT Debt: Part II – The Performance Paradox

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Some of the business ramifications of the $1 trillion in IT debt have been explored in the first post of this two-part analysis. This second post focuses on “an ounce of prevention is worth a pound of cure” aspects of IT debt. In particular, it proposes an explanation why prevention was often neglected in the US over the past decade and very possibly longer. This explanation is not meant to dwell on the past. Rather, it studies the patterns of the past in order to provide guidance for what you could do and should do in the future to rein in technical debt.

The prevention vis-a-vis cure trade-off  in software was illustrated by colleague and friend Jim Highsmith in the following figure:

Figure 1: The Technical Debt Curve

As Jim astutely points out, “once on far right of curve all choices are hard.” My experience as well as those of various Cutter colleagues have shown it is actually very hard. The reason is simple: on the far right the software controls you more than you control it. The manifestations of technical debt [1] in the form of pressing customer problems in the production environment force you into a largely reactive mode of operation. This reactive mode of operation is prone to a high error injection rate – you introduce new bugs while you fix old ones. Consequently, progress is agonizingly slow and painful. It is often characterized by “never-ending” testing periods.

In Measure and Manage Your IT Debt, Gartner’s Andrew Kyte put his finger on the mechanics that lead to the accumulation of technical debt – “when budget are tight, maintenance gets cut.” While I do not doubt Andrew’s observation, it does not answer a deeper question: why would maintenance get cut in the face of the consequences depicted in Figure 1? Most CFOs and CEOs I know would get quite alarmed by Figure 1. They do not need to be experts in object-oriented programming in order to take steps to mitigate the risks associated with slipping to the far right of the curve.

I believe the deeper answer to the question “why would maintenance get cut in the face of the consequences depicted in Figure 1?” was given by John Seely Brown in his 2009 presentation The Big Shift: The Mutual Decoupling of Two Sets of Disruptions – One in Business and One in IT. Brown points out five alarming facts in his presentation:

  1. The return on assets (ROA) for U.S. firms has steadily fallen to almost one-quarter of 1965 levels.
  2. Similarly, the ROA performance gap between corporate winners and losers has increased over time, with the “winners” barely maintaining previous performance levels while the losers experience rapid performance deterioration.
  3. U.S. competitive intensity has more than doubled during that same time [i.e. the US has become twice as competitive – IG].
  4. Average Lifetime of S&P 500 companies [declined steadily over this period].
  5. However, in those same 40 years, labor productivity has doubled – largely due to advances in technology and business innovation.

Discussion of the full-fledged analysis that Brown derives based on these five facts is beyond the scope of this blog post [2]. However, one of the phenomena he highlights –  “The performance paradox: ROA has dropped in the face of increasing labor productivity” – is IMHO at the roots of the staggering IT debt we are staring at.

Put yourself in the shoes of your CFO or your CEO, weighing the five facts highlighted by Brown in the context of Highsmith’s technical debt curve. Unless you are one of the precious few winner companies, the only viable financial strategy you can follow is a margin strategy. You are very competitive (#3 above). You have already ridden the productivity curve (#5 above). However, growth is not demonstrable or not economically feasible given the investment it takes (#1 & #2 above). Needless to say, just thinking about being dropped out of the S&P 500 index sends cold sweat down your spine. The only way left to you to satisfy the quarterly expectations of Wall Street is to cut, cut and cut again anything that does not immediately contribute to your cashflow. You cut on-going refactoring of code even if your CTO and CIO have explained the technical debt curve to you in no uncertain terms. You are not happy to do so but you are willing to pay the price down the road. You are basically following a “survive to fight another day” strategy.

If you accept this explanation for the level of debt we are staring at, the core issue with respect to IT debt at the individual company level [3] is how “patient” (or “impatient”) investment capital is. Studies by Carlota Perez seem to indicate we are entering a phase of the techno-economic cycle in which investment capital will shift from financial speculation toward (the more “patient”) production capital. While this shift is starting to happens, you have the opportunity to apply “an ounce of prevention is worth a pound of cure” strategy with respect to the new code you will be developing.

My recommendation would be to combine technical debt measurements with software process change. The ability to measure technical debt through code analysis is a necessary but not sufficient condition for changing deep-rooted patterns. Once you institute a process policy like “stop the line whenever the level of technical debt rose,” you combine the “necessary” with the “sufficient” by tying the measurement to human behavior. A possible way to do so through a modified Agile/Scrum process is illustrated in Figure 2:

Figure 2: Process Control Model for Controlling Technical Debt

As you can see in Figure 2, you stop the line and convene an event-driven Agile meeting whenever the technical debt of a certain build exceeds that of the previous build. If ‘stopping the line’ with every such build is “too much of a good thing” for your environment, you can adopt statistical process control methods to gauge when the line should be stopped. (See Using 3σ  Control Limits in Software Engineering for a discussion of the settings appropriate for your environment.)

An absolutely critical question this analysis does not cover is “But how do we pay back our $1 trillion debt?!I will address this most important question in a forthcoming post which draws upon the threads of this post plus those in the preceding Part I.

Footnotes:

[1] Kyte/Gartner define IT Debt as “the costs for bringing all the elements [i.e. business applications] in the [IT] portfolio up to a reasonable standard of engineering integrity, or replace them.” In essence, IT Debt differs from the definition of Technical Debt used in The Agile Executive in that it accounts for the possible costs associated with replacing an application. For example, the technical debt calculated through doing code analysis on a certain application might amount to $500K. In contrast, the cost of replacement might be $250K, $1M or some other figure that is not necessarily related to intrinsic quality defects in the current code base.

[2] See Hagel, Brown and Davison: The Power of Pull: How Small Moves, Smartly Made, Can Set Big Things in Motion.

[3] As distinct from the core issue at the national level.