Semantic Werks

“Improving change tolerance through Capabilities-based design: an empirical analysis“, by Ramya Ravichandar, James D. Arthur, Shawn A. Bohner and David P. Tegarden. J. Softw. Maint. Evol.: Res. Pract. 2008; 20:135–170

The scope of this paper is building software systems that are change tolerant, where “the term ‘change tolerance’ connotes the ability of software to evolve within the bounds that it was designed”. It’s important to keep in mind the phrase “within the bounds” — this limits this technique to de novo design, rather than adaptive design (i.e., design that admits new components after the initial implementation). Any design that is designed to be change tolerant necessarily requires identification of potential changes up-front. And the authors acknowledge this with a small dig at agile techniques as being ‘unconventional’ (not anymore) and unproven (unlike the enormous body of evidence supporting waterfall processes :).

It would seem the focus of the paper is on complex, emergent systems: “requirements and technology often evolve during the extended development periods of complex emergent systems, and thereby, inhibit a comprehensive up-front solution specification” (agreed); yet the empirical assessment is of a course evaluation system. They measure how many classes are impacted by each change to assess how CE reduces change impact.

There are several problems with their empirical assessment. First, they compare “RE-based design” with their technique. They obtain the RE-design by reverse engineering an existing system, but they have not validated it as a ‘good’ design (we need to compare apples and apples, right — both designs should have been created using the same amount of effort and skill). Second, the selection of change events is questionable; that the authors chose the change events implies bias in choosing those the CE-design is suited for. Finally, this system seems excessively trivial for a complex emergent system, as I mentioned before. Of course, it isn’t easy to evaluate truly complex systems.

Capability engineering is a combination of abstraction, reduced coupling, and high cohesion design (which really tells us nothing — who out there is designing systems that have high coupling and low cohesion? Possibly a few embedded systems, but not much else, I would wager). The key to the whole thing seems to be the functional decomposition, which is independent of system architecture/specification, and derived from user requirements. This commits the process fairly heavily to up-front requirements elicitiation, which is odd, since they claim to be concerned with complex, emergent systems, where (to my mind) requirements are never completely known from the beginning.

Complaints:

• no reference is made to feature modeling, which seems nearly identical;
• a bad case of EMS (Excessive Math Syndrome): too much detail about cohesion metrics, or algorithms for system design, when the real issue is coming up with the source numbers (anyone remember GIGO?);
• conclusion marred by the confounding factor that the RE-system might just be poorly designed;
• no reference to work on autonomic monitoring and correction (see Yiqiao’s work).

Full marks:

• acknowledging the issue of change in software systems (although I’m biased)
• actually proposing a theory and testing it!
• discussing threats to validity

What I liked the most about this paper was its fairly scientific setup. I don’t agree that capability engineering is the panacea, but at least I can make concrete objections thanks to their well-structured paper.

On an unrelated Google expedition, I came across the ISBSSG, the International Software Standards Benchmarking Group. Their mission is to collect software and IT project data for comparison and metric purposes.

This seems like a great idea, and although I can’t find details, the fees for academic access are listed as ‘nominal’. Has anyone had experience with this group? I wonder what the data are like, in terms of representativeness, accuracy, usability.

Such a repository would provide some solution to the problem of data independence (e.g., not IBM-sponsored) and access.

On a somewhat related note: a recent study of cancer experiments showed that of the clinical trials registered with the FDA (which is mandatory), only 18% were reported in academic journals. Of the industry-sponsored trials, 75% were positive findings. While this seems bad, I bet the majority of unpublished studies were rejected by the peer-review process as not worth publishig. Still, the negative results ought to be published somewhere, even as a one-page report (via the excellent ‘Bad Science‘ blog).

Reading Robert Yin’s book on case studies has really opened my eyes. To date the most contentious aspect has been his distinction between statistical generalization and analytic generalization. The former is what is done in surveys and experiments: using a sample to make statements about the population. The latter is what he argues is done during a case study. A case study is not a sample, but rather part of an argument.

To properly frame the debate, I think it is important to start with the question of epistemology. That is, what is it we are seeking to do when we do research? In my opinion, it is to motivate the rational beliefs we can hold about a given observable phenomenon in the universe. For example, do requirements evolve? If they evolve, what is being done about it? Etc., etc.

Once we have a list of questions we are interested in — why is this apple hitting my head while the moon is always up in the sky? — we can begin to try and answer them. This is where the scientific method chosen is important. In phenomena whose causes can be controlled for, an experimental approach is useful. It allows us to generalize our results to a wider population. But a lot of phenomena in the world are not easily controllable. In this case, we need a technique that can allow us to still draw some conclusions about the phenomenon of interest. Otherwise, we would just throw up our hands in disgust. Is agile software development better than waterfall? Choosing a few good (representative) cases might well let us make some conclusions.

I think one of the important things to remember is that while case studies seem easy to do — just pick a subject and write up a history of the project — they are in reality much more complex to set up than their experimental cousins. This duality is what has led many to discount their scientific — epistemological — value. Most of what is called ‘case study’ in the literature is really more akin to ‘proof-of-concept’.