Facts of Software Engineering Management

This chapter is from the book

Facts and Fallacies of Software Engineering

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This chapter is from the book

This chapter is from the book 

Facts and Fallacies of Software Engineering

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Tools and Techniques

Discussion

Time was, way back when, that new software engineering ideas were really breakthroughs. High-order programming languages. Automated tools like debuggers. General-purpose operating systems. That was then (the 1950s). This is now. The era of breakthrough techniques, the things that Fred Brooks (1987) referred to as silver bullets, is long since over.

Oh, we may have fourth-generation languages ("programming without programmers") and CASE tools ("the automation of programming) and object orientation ("the best way to build software") and Extreme Programming ("the future of the field") and whatever the breakthrough du jour is. But, in spite of the blather surrounding their announcement and advocacy, those things are simply not that dramatically helpful in our ability to build software. And, to paraphrase Brooks himself, the most rational viewpoint to take on breakthroughs is "not now, not ever." Or perhaps, "unlikely ever again."

In fact, there is some pretty solid data to that effect. Nearly all so-called breakthroughs, circa 1970 to today, are good for modest benefits (less than 35 percent) for software engineers. Considering that the breakthrough blather is making claims for "order of magnitude" improvements (that is, powers of 10), there is a huge gap between these claims and reality.

The evidence on this subject is quite strong. In my own longitudinal research, examining evaluative studies by objective researchers of the value of these improvements (Glass 1999), I have found

• A serious lack of evaluative research because there are few such studies to draw on.

• Enough studies to be able to draw some significant conclusions.

• A nearly total lack of any evidence that any of these things has breakthrough benefits.

• Fairly solid evidence that the benefits are indeed there, but at a far more modest level—5 to 35 percent.

(The references and further readings in that paper can point you to the original studies that produced this objective evaluative data.)

These findings are echoed in a wonderful table in a best of practice book on software process improvement (Grady 1997), in which the author lists some of the various process changes that are part of a process improvement program and the benefits that may be achieved by them. What was the highest benefit process change, you may ask? Reuse. The payoff for reuse, according to Grady, is 10 to 35 percent. Contrast that with the extravagant "order of magnitude" claims of the componentry zealots of today. Or the claims of any of the other zealots of yesteryear.

Why do we go through this cycle of hype-and-dashed-hopes again and again? It takes two kinds of people to sustain this cycle—the hypesters themselves and the true believers. The hypesters, as it turns out, almost always are nonobjective folks who have something to gain—product sales, high-priced courses, or funding for research projects. 'Twas always thus. Since the days of the Universal Elixir peddlers, there have always been people eager to make a fast buck on promises unsubstantiable by realities.

The ones who worry me, given that there will always be fast-buck pursuers, are those true believers. Why do those folks believe, again and again and again, the promises of the hypesters? Why are we subjected, again and again and again, to massive expenditure and training in new concepts that cannot possibly deliver what is claimed for them? Answering that question is one of the most important tasks of our field. If answering it were easy, we wouldn't have to ask it. But I will try to suggest answers in the material that follows.

Controversy

I've never met anyone who disagreed with the notion that there is too much hype in the software field. But behavior, all too often, clashes with this belief. Nearly everyone, at the conceptual level, agrees with the Brooks notion that there is unlikely to be any silver bullet forthcoming. But so many people, when push really gets down to shove, leap eagerly aboard the latest software engineering breakthrough bandwagon.

Sometimes I think what is happening is a kind of "hardware envy." Our computer hardware brethren have made remarkable progress in the few decades that computer hardware has been produced. Cheaper/better/faster happens over and over again in that hardware world. Friends who study the history of technology tell me that progress in the computer hardware field is probably faster than that in any other field, ever. Perhaps we in software are so envious of that progress that we pretend that it is happening—or can happen—to us.

There is another thing going on, as well. Because the whole hardware/software thing has moved forward so dramatically and so rapidly, there is a great fear of being left behind and a great eagerness to participate in whatever is new. We draw up life cycles of new process/product innovation, and cheer for the "early adopters" while booing the "laggards." In the computing field there is a whole cultural thing that says new is better than old. Given all of that, who indeed would not want to embrace the new and step away from the old? In that emotional climate, buying into hype is a good thing, and stepping up in front of the hype steamroller is bad.

What a shame all of that is. The software field has been victimized so many times by its hypesters and their fellow travelers. And, to make matters worse, I'd be willing to bet that—as you are reading this—there is some other new idea parading down the pike, its zealots leading the way and claiming dramatic benefits, your colleagues dancing merrily along in their wake. The Pied Piper of hype is striking yet again!

Sources

The pronouncements of the fact of hype are severely outnumbered by the purveyors of faulty promise. I can't remember back far enough to identify the first published hypester, and I wouldn't want to give them "credit" here if I could. But it has all been going on for a very long time. My very first book (1976), for example, was titled The Universal Elixir, and Other Computing Projects Which Failed and told a few tales that ridiculed these computing elixir-selling hypesters. Those stories had originally been published in Computerworld (under an assumed name, Miles Benson) for a decade before they were gathered into that book.

There are far more sources that promise panaceas then there are voices of reason crying in this wilderness. But here, as well as in the References that follow, are a few of those voices:

Davis, Alan M. 1995. 201 Principles of Software Development. New York: McGraw-Hill. Principle 129 is "Don't believe everything you read."

Weinberg, Gerald. 1992. Quality Software Development: Systems Thinking. Vol. 1, p. 291. New York: Dorset House.

References

Brooks, Frederick P., Jr. 1987. "No Silver Bullet—Essence and Accidents of Software Engineering." IEEE Computer, Apr. This paper has been published in several other places, most notably in the Anniversary Edition of Brooks's best-known book, The Mythical Man-Month (Reading, MA: Addison-Wesley, 1995), where it is not only included in its original form but updated.

Glass, Robert L. 1976. "The Universal Elixir, and Other Computing Projects Which Failed." Computerworld. Republished by Computing Trends, 1977, 1979, 1981, and 1992.

Glass, Robert L. 1999. "The Realities of Software Technology Payoffs." Communications of the ACM, Feb.

Grady, Robert B. 1997. Successful Software Process Improvement. Table 4-1, p. 69. Englewood Cliffs, NJ: Prentice-Hall.

Discussion

Learning a new tool or technique, assuming that there is value associated with its use, is a good thing. But perhaps not as good as the early adopters might have us believe. There is a cost to learning to use new ideas. We must come to understand the new idea, see how it fits into what we do, decide how to apply it, and consider when it should and shouldn't be used. Being forced to think about things that previously have been pretty automatic for us slows us down.

Whether the new idea is using a test coverage analyzer tool for the first time and figuring out what that means to our testing process or trying out Extreme Programming and adjusting to all the new techniques it contains, the user of the new idea will be less efficient and less effective. That does not mean that these new ideas should be avoided; it simply means that the first project on which they are employed will go more slowly, not faster, than usual.

Improving productivity and product quality have been the holy grails of software process for the last couple of decades. The reason we adopt new tools and techniques is to improve productivity and quality. So it is an irony of the technology transfer process that productivity and quality initially go down, not up, when we change gears and try something new.

Not to worry. If there truly is benefit to the new thing, eventually it will emerge. But that brings up the question "how long?" What we are talking about is the learning curve. In the learning curve, efficiency and effectiveness dip at the outset, rise back past the norm, and eventually plateau at whatever benefit the new thing is capable of achieving. Given that, the "how long" question translates into several "how longs." How long will we have diminished benefits? How soon do we return to normal benefits? How long before we get the ultimate benefits?

At this point, what all of us would like to be able to say is something like "three months" and "six months." But, of course, none of us can say that—or anything else, for that matter. The length of the learning curve is situation- and environment-dependent. Often, the higher the benefit at the end, the longer the learning curve. To learn object orientation superficially might take three months, but to become proficient at it might take two fully immersed years. For other things, the lengths would be totally different. The only predictable thing is that there will be a learning curve. One can draw the curve on a chart, but one cannot put any meaningful scale on that chart's axes.

The one remaining question is "how much?" How much benefit will the new idea bring us? That is just as unknowable as the answers to the "how long" questions. Except for one thing. Because of what we learned in the previous fact, it is most likely true that the benefit will be 5 to 35 percent higher than was achieved before the new idea was assimilated.

There is one more important factor to add. Although we cannot give any general answers to the "how long" and "how much" questions, there are answers. For any new concept—test coverage analyzers or Extreme Programming, for example—those with experience can help you with those questions. Find people who already have assimilated the concept you want to embrace, through your peer network, user group, professional society, and so on, and inquire about how long it took them (they are quite likely to know the answer to that question) and how much benefit they achieved (that's a tougher question, and unless they are users of software metrics approaches, they may not know). And don't forget, while you're asking, to find out what lessons they learned—pro and con—in their adoption process.

Be sure to avoid zealots when you ask those questions, of course. Zealots sometimes give the answers they believe, rather than the answers they experienced.

Controversy

There shouldn't be any controversy over this matter. It is obvious that there is cost attached to learning something new. But, in fact, there often is controversy. The claims of zealots for huge benefits and quick learning curves all too often trans-late into management belief in those benefits and in that quick learning process (see Fact 5). Managers expect new approaches, when employed, to work right out of the box. Under those circumstances, cost and schedule estimates are made with the assumption that the benefits will be achieved from the beginning.

In a classic story to this effect, one pharmaceutical company, installing the SAP megapackage, bid low on some contracts because they assumed the benefits of SAP would be achieved immediately. That company disappeared, a learning curve period later, in a flame of bankruptcy and litigation. "Don't try this at home" might be the lesson learned here.

Source

The learning curve and its impact on progress is described in many places, including

Weinberg, Gerald. 1997. Quality Software Management: Anticipating Change. Vol. 4, pp. 13, 20. New York: Dorset House.

Discussion

Tools are the toys of the software developer. They love to learn about new tools, try them out, even procure them. And then something funny happens. The tools seldom get used.

A colorful term emerged from this tendency a decade or so ago. During the height of the CASE tools movement, when everyone seemed to believe that CASE tools were the way of the software future, a way that might well automate the process of software development, lots and lots of those CASE tools were purchased. But so many of them were put on the figurative shelf and never used, that the term shelfware was invented to describe the phenomenon.

I was a victim of that whole movement. At the time, I frequently taught a seminar on software quality. In that seminar, I expressed my own belief that those CASE tools were nowhere near the breakthrough technology that others claimed them to be. Some of my students sought me out after one session to tell me that I was out-of-date on the subject of CASE. They were convinced, as I was not, that these tools were indeed capable of automating the software development process.

I took those students seriously and immediately immersed myself in the CASE body of knowledge to make sure that I hadn't indeed missed something and become out-of-date. Time passed. And along with the passage of time came vindication for my point of view. CASE tools were indeed beneficial, we soon learned, but they were definitely not magical breakthroughs. The shelfware phenomenon resulted, among other things, from those dashed hopes.

But I digress. This fact is not about tools seen to be breakthroughs (we skewered that thought in Fact 5), but rather tools as successful productivity enhancers. And, for the most part, they are that. So why do these kinds of tools also end up on the shelf?

Remember Fact 6 about the learning curve? The one that says that trying out a new tool or technique, far from immediately improving productivity, actually diminishes it at the outset? Once the thrill of trying out a new tool has worn off, the poor schedule-driven software developer must build real software to real schedules. And, all too often, the developer reverts to what he or she knows best, the same tools always used. Compilers for the well-known programming language they know and love. Debuggers for that language. Their favorite (probably language-independent) text editors. Linkers and loaders that do your bidding almost without your thinking about it. Last year's (or last decade's) configuration management tools. Isn't that enough tools to fill your toolbox? Never mind coverage analyzers or conditional compilers or standards-conformance checkers or whatever the tool du jour might be. They might be fun to play with, these developers say to themselves, but they're a drag when it comes to being productive.

There is another problem about tools in the software field, in addition to the one we have already discussed. There is no "minimum standard toolset," a definition of the collection of tools that all programmers should have in their toolboxes. If there were such a definition, programmers would be much more likely to use at least the tools that made up that set. This is a problem that no one seems to be working on. (At one time, IBM proposed a toolset called AD/Cycle, but it made a dull thud in the marketplace—it was too expensive and too poorly thought through—and no one has tried to do that since.)

Controversy

Often software practitioners are tagged with the "not-invented-here" (NIH) phenomenon. They are accused of preferring to do their own thing rather than building on the work of others.

There is, of course, some of that in the field. But there is not as much as many seem to think. Most programmers I know, given the choice of something new or something old, will use the something new, but only if they are sure that they can complete their tasks at hand more quickly if they do. Since that is seldom the case (there's that old learning curve problem again), they revert to the old, the tried, the true.

The problem here, I would assert, is not NIH. The problem is a culture that puts schedule conformance, using impossible schedules, above all else—a culture that values schedule so highly that there is no time to learn about new concepts. There are tools catalogs (for example, ACR) that describe the what/where/how of commercial tools. Few programmers are aware of the existence of many of the tools of our trade. Fewer still are aware of the catalogs that could lead to them. We will return to these thoughts in the facts that follow.

Regarding any controversy over a minimum standard toolset, so few are giving that any thought that there is no controversy whatsoever. Imagine what wonderful controversy could result if people began thinking about it.

Sources

ACR. The ACR Library of Programmer's and Developer's Tools, Applied Computer Research, Inc., P.O. Box 82266, Phoenix AZ 85071-2266. This was an annually updated software tools catalog, but has suspended publication recently.

Glass, Robert L. 1991. "Recommended: A Minimum Standard Software Toolset." In Software Conflict. Englewood Cliffs, NJ: Yourdon Press.

Wiegers, Karl. 2001. Personal communication. Wiegers says, "This fact is something I've said repeatedly. It has been published in an interview I once did, conducted by David Rubinstein and appearing in Software Development Times, Oct. 15, 2000."