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Fallacies of Software Engineering Management

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The usefulness of metrics, managing quality, and ego-less programming are all fallacies. Learn how software engineering management works in the real world.
This chapter is from the book

Fallacy 1 You can't manage what you can't measure.


The purpose of this saying is to point out that measurement is invaluable to managers. Clearly, managers need to know the answer to questions such as how much, when, and how well. There is a whole software engineering subfield devoted to the topic of software metrics, and proposals of new things to count—and how to count things long understood—are rampant.

What is interesting about the field of software metrics is that it is used little in practice. In surveys of tools and techniques used by software managers, metrics generally come in close to last. There are exceptions, of course—some enterprises (for example, IBM, Motorola, and Hewlett-Packard) place heavy emphasis on metric approaches. But for the most part, metric approaches are soundly ignored. Why is that? Perhaps managers are unconvinced of the value of metrics. Perhaps some of the necessary data is too hard to collect.

But there have been lots of studies of both the value and the cost of metrics, most of which have positive findings. At NASA-Goddard, for example, studies have shown that the ongoing cost of collecting the necessary metrics should be no more than 3 percent (data collection and analysis) + 4 to 6 percent (processing and analyzing the data) = 7 to 9 percent of the total cost of the project (Rombach 1990). NASA-Goddard considers that to be a bargain, given the value of their results.

Some of the history of metrics approaches has been tainted, however. Originally, managers all too often collected data that didn't matter or that cost too much to obtain. Such helter-skelter metrics collection was expensive and, as it turned out, pointless. It wasn't until the notion of the GQM approach (originally proposed by Vic Basili)—establish Goals to be satisfied by the metrics, determine what Questions should be asked to meet those goals, and only then collect the Metrics needed to answer just those questions—that there began to be some rationality in metrics approaches.

There was also the problem of software science. Software science was an attempt by the brilliant computing pioneer Murray Halstead to establish an underlying science for software engineering (Halstead 1977). He defined factors to measure and ways of measuring them. It seemed a worthy and, at the time, an important goal. But study after study of the numbers obtained showed neutral or negative value to the software science data. Some even likened software science to a form of astrology. The collection of "scientific" data about software projects eventually fell into disrepute and has, for the most part, been abandoned. Those who remember the software science debacle tend to taint all software metrics activities with the same brush.

Nevertheless, the collection of software metric data now happens often enough that there is even a "top 10" list of software metrics, the ones most commonly used in practice. To present an answer to the question "what are software metrics?" we present that list here.

Software Metrics

% Reported Using

Number of defects found after release


Number of changes or change requests


User or customer satisfaction


Number of defects found during development


Documentation completeness/accuracy


Time to identify/correct defects


Defect distribution by type/class


Error by major function/feature


Test coverage of specifications


Test coverage of code


Perhaps equally interesting is the list of the bottom 5 metrics:

Software Metrics

% Reported Using

Module/design complexity


Number of source lines delivered


Documentation size/complexity


Number of reused source lines


Number of function points


(This data comes from Hetzel [1993]. There is no reason to believe that the intervening years since 1993 would have changed this list a great deal, although advocates of function points claim a recent rise in their usage.)


The problem with the saying "you can't manage what you can't measure"—what makes it a fallacy—is that we manage things we can't measure all the time. We manage cancer research. We manage software design. We manage all manner of things that are deeply intellectual, even creative, without any idea of what numbers we ought to have to guide us. Good knowledge worker managers tend to measure qualitatively, not quantitatively.

The fact that the saying is a fallacy should not cause us to reject the underlying truth of the message it brings, however. Managing in the presence of data is far better and easier than managing in its absence. In fact, it is the nature of managers—and human beings in general—to use numbers to help us understand things. We love batting and fielding and earned run averages. We love basket and rebound and assist counts and invent terms like triple double to accommodate combinations of them. We even invent data to cover subjects when there is no natural data, such as ice skating and diving (where judges assign scores to performances).

This is a case in which the fact is that measurement is vitally important to software management, and the fallacy lies in the somewhat-cutesy saying we use to try to capture that.


The saying "you can't manage what you can't measure" appears most frequently in books and articles on software management, software risk, and (especially) software metrics. An interesting thing happened when I set out to track down where the saying originally came from. Several metrics experts said that it came from Controlling Software Projects (DeMarco 1998), and so I got in touch with Tom DeMarco himself. "Yes," said DeMarco, "it's the opening sentence in my book, Controlling Software Projects. But," he went on to say, "the saying is actually 'you can't control what you can't measure.'" Thus the fallacy version of the saying is actually a corruption of what DeMarco really said!


DeMarco, Tom. 1998. Controlling Software Projects: Management, Measurement, and Estimation. Englewood Cliffs, NJ: Yourdon Press.

Halstead, M.H. 1977. Elements of Software Science. New York: Elsevier Science.

Hetzel, Bill. 1993. Making Software Measurement Work. Boston: QED.

Rombach, H. Dieter. 1990. "Design Measurement: Some Lessons Learned." IEEE Software, Mar.

Fallacy 2 You can manage quality into a software product.


This is a reprise of an idea presented previously in this book. In the section About Quality, I asked the question "whose responsibility is quality? My answer, as you may remember, was that no matter how many people believed" that management was responsible for product quality, there was too much technology to the subject of software quality to leave it up to management. I then went on at that point to say that nearly every one of the quality "-ilities" had deeply technical aspects, aspects that only a technologist could work with.

Not only is the achievement of quality a technical task, but those who believe that it is a management task often go about it in the wrong way. Over the years, managers have tried to use motivational campaigns to instill a quality viewpoint, as if the average technologist would be interested only in quality if he or she were pushed to do so. Sloganeering—"Quality Is Job One"—and methodologizing—"Total Quality Management"—seem to be management's chief approaches to achieving software product quality. Far from accepting these approaches, technologists tend to be alienated by them. And it doesn't help that the chief enemy of product quality on most software projects is schedule pressure. Management is motivating and methodologizing with one hand and applying antiquality schedule pressure with the other. It is simply not possible for those managers to have it both ways. And most technologists are smart enough to know that.

So what's the fallacy here? That quality is a management job. Management, of course, does have a vitally important role in achieving quality. They can establish a culture in which the task of achieving quality is given high priority. They can remove barriers that prevent technologists from instituting quality. They can hire quality people, by far the best way of achieving product quality. And they can get out of the way of those quality people, once the barriers are down and the culture is established, and let them do what they have wanted to do all along—build something they can be proud of.


The controversy here is plentiful. When I learned I was to teach the course on software quality in the Software Engineering Master's degree program at Seattle University, I needed to pick a textbook. Every choice I examined was management-focused. To tell the quality story the way I believed it had to be told, I eventually wrote my own textbook for the course. To this day, most of the other books on quality are still management-focused.


Glass, Robert L. 1992. Building Quality Software. Englewood Cliffs, NJ: Prentice-Hall. There is a parable on page 246 that personifies the problems of attempting to manage quality into software. The two last lines of the parable are

But who is looking out for product quality?

No one answered.

DeMarco, Tom, and Timothy Lister. 1999. Peopleware. 2d ed. New York: Dorset House. These authors find management approaches to quality particularly distasteful. They describe management motivational campaigns as "those damn posters and plaques." The effect on a team of using this approach, they say, is "teamicide." They refer to these posters and plaques as "phony" and say that they "make most people's skin crawl." You get the general idea that these authors don't care for the usual management approaches for achieving quality.

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