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Posts Tagged ‘Source Code Analysis

Should You Ship This Code Before Reducing Technical Debt?!

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File:Control flow graph of function with loop and an if statement without loop back.svg

Source: JulesH, Wikipedia, A control flow graph of a simple function

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.

mccabegraph.jpg

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.

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.

Should You Invest in This Software?!

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Martin Fowler - Technical debt quadrant by Kalle Hoppe.

Source: martinfowler.com/bliki/TechnicalDebtQuadrant.html

Consider the following scenario: You are a venture capitalist. One of your portfolio companies has been working for a few years on a promising software application. Various surprises with respect to schedule and functionality have been sprung on you along the way. The company now asks for one last shot-in-the-arm in order to get the product out the door, market and sell it. Should you open your wallet one more time to fund this alleged last push?

It is a familiar scenario not only for venture capitalists, but for CEOs, CFOs, general managers and M&A executives. A renowned CEO once told me the following when I pushed my luck with respect to project funding:

Israel, I have a warehouse of software products that never generated a dime for me.

Believe me, this CEO was neither amused nor philosophical…

Code analysis techniques have progressed to the point that the answer to the software investment question for object-oriented code can to a certain extent be determined  through quantifying technical debt. For example, assume the following circumstances:

  • A company expects to ship 500K lines of code in 6 months.
  • The company asks for additional $2M to complete development and make a significant resound in the market.

To assess the investment decision, apply the code analysis techniques described in Using Credit Limits to Constrain Development on Margin to quantify the technical debt.  Assuming a debt of $2 per line of code has been identified, the overall technical debt amounts to $1M (2X500K).

The investment decision then is not an incremental $2M decision. It is actually a $3M ($2M+1M) investment decision when the technical debt is taken into account.  The technical debt might not need to be paid overnight, but it will have to be paid back over a period of time. The team might not hire additional resources to reduce/eliminate the technical debt, but the team resources dedicated to reducing technical debt will not be available  to carry out other assignments. Hence, the opportunity cost ($1M) is real, relevant and should be taken into account.

If you are hesitant to continue investing in this software/team, you stare at a tricky question:

  • What will it take to start afresh?

If you decide to make the $3M investment, two operational questions pose themselves:

  • How should work on reducing/eliminating technical debt be interleaved with other pressing work such as new functions and features?
  • Given a $1M debt on 500K lines of code, can the company indeed ship as expected in 6 months?

We will address these three questions in forthcoming posts in The Agile Executive.

Written by israelgat

March 4, 2010 at 5:40 am

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