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First IEEE Computer Society Guidebook on Kindle

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A Kindle edition of The Concise Executive Guide to Agile is available now through Amazon. It is the first IEEE Computer Society guidebook designed for and issued on an electronic reader. My sentiments about being the IEEE pilot project for Kindle are expressed in the following quote from yesterday’s press release:

“How appropriate it is that a book on Agile software methods was chosen as the pilot Kindle project by the prestigious IEEE Computer Society,” said Israel Gat.  “The reach and richness of Kindle make it an ideal vehicle for effectively disseminating the Agile message to audiences that so far have not been touched by it.”

I am indebted to Kate Guillemette and Linda Shafer who made this IEEE pilot project happen.

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Written by israelgat

July 13, 2010 at 7:17 am

Using 3σ Control Limits in Software Engineering

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Source: Wikipedia; Control Chart

The  July/August 2010 issue of IEEE Software features an article entitled “Monitoring Software Quality Evolution for Defects” by Hongyu Zhang and Sunghun Kim. The article is of interest to the software developer/tester/manager in quite a few ways. In particular, the authors report on their successful use of 3σ control limits in c-charts used to plot defects in software projects.

To put things in perspective, consider my recent assessment of the results accomplished by Quick Solutions (QSI) in two of their projects:

One to one-and-a-half standard deviation better than the mean might not seem like much to six-sigma black belts. However, in the context of typical results we see in the software industry the QSI results are outstanding.  I have not done the exact math whether those results are superior to 95%, 97% or 98% of software projects in Michael Mah‘s QSMA database as the very exact figure almost does not matter when you achieve this level of excellence.

A complementary perspective is provided by Capers Jones in Estimating Software Costs: Bringing Realism to Estimating:

Another way of looking at six-sigma in a software context would be to achieve a defect-removal efficiency level of about 99.9999 percent. Since the average defect-removal efficiency level in the United States is only about 85 percent, and less than one project in 1000 has ever topped 98 percent,  it can be seen that actual six-sigma results are beyond the current state of the art.

The setting of control limits is, of course, quite a different thing from the actual defect-removal efficiency numbers reported by Jones for the US and the very low number of defects reported by Mah for QSI. Having said that, driving a continuous improvement process through using 3σ control limits is the best recipe toward eventually reaching six-sigma results. For example, one could drive the development process by using Cyclomatic complexity per Java class as the quality characteristic in the figure at the top of this post. In this figure, a Cyclomatic complexity reading higher than 10.860 (the Upper Control Limit) will indicate a need to “stop the line” and attend to reducing complexity before resuming work on functions and features.

Coming on the heels of the impressive results reported by David Joyce on the use of statistical process control (SPC) techniques by the BBC, the article by Zhang and Kim is another encouraging report on the successful application of manufacturing techniques to software (and to knowledge work in general). I am not at liberty to quote from this just published IEEE article, but here is the abstract:

Quality control charts, especially c-charts, can help monitor software quality evolution for defects over time. c-charts of the Eclipse and Gnome systems showed that for systems experiencing active maintenance and updates, quality evolution is complicated and dynamic. The authors identify six quality evolution patterns and describe their implications. Quality assurance teams can use c-charts and patterns to monitor quality evolution and prioritize their efforts.

The Concise Executive Guide to Agile

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The IEEE Computer Society Press published today a ReadyNote[1] that I authored entitled The Concise Executive Guide to Agile. It is available through the IEEE Computer Society Store. A Kindle version will be published in June.

I had two main objectives in writing the guide:

  1. Provide the know-how for approaching Agile in a concise manner that requires minimal investment of time and effort by the reader. The ReadyNote does so by summarizing most Agile topics in a page or two. Detailed coverage of a topic is left for follow-on reading in the selected references that accompany each topic and in the Further Reading appendix.
  2. Be accessible to any executive  — R&D, Marketing, Sales, Program Management, Professional Services, Customer Support, Finance, or IT.  The only assumption I make is that the reader has a conceptual grasp of software and software engineering as well as an interest in learning about Agile. No deep knowledge, let alone technical knowledge, about software engineering is required for comprehending the guide.

There is no fluff in the guide. Every paragraph has been written to satisfy the “And therefore what?!” criterion. It is my intent to drive a point home and make it clear to the reader what he/she could do with the information in as few words as possible.

A simple acid test for the guide is your successful assimilation of it in entirety during a flight in the continental US. Something has not quite worked if you need to fly all the way from the US to Europe or vice versa in order to comprehend the guide…

I would like to express my sincere thanks to Michael Cote, Michael Mah, Hubert Smits and to the fourth reviewer (whose identity I don’t know) for their many helpful insights and suggestions. I am also deeply indebted to Linda Shafer and Kate Guillemette of the IEEE Computer Society who got me to write the guide and supported the writing and editing process along the way.

Enjoy reading!

Footnotes:

  1. “ReadyNotes are short e-books that are tightly focused on specific topics” [IEEE Computer Society Press].

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.