Since PDA-like devices date back to the 1980s, and John Sculley announced the Apple Newton — complete with a touch screen — in early 1992, a year before the earlier patent was filed, one wonders whether these patents can hold up under prior art, and it’s unclear why the patents were granted in the first place.

On the other hand, there are some curious information gaps on the web. Just last week (see entry for 05:29, 18 June 2008), someone stripped out the “History” section of the Wikipedia article on PDAs, which — before being deleted — documented PDAs going back to 1983. Likewise, a commonly-linked article — “The Evolution of the PDA: 1975-1995″ by Evan Koblanz — appears to have been pulled off the web. (Google doesn’t have a cached version, nor does the Internet Wayback Machine.)

Sounds as though someone may be trying to do historical revision in advance of trial. ..bruce..

Categories: managerial

Various industry studies cite the productivity gap between the best and the worst developers. While there is some controversy over the ranges often cited (such as the famous 26:1 figure), anyone who has managed a diverse group of developers won’t argue with the fact that there can be a dramatic difference in productivity among individuals.

What is less obvious is that given a group of developers who are roughly equal in having a sufficient or even superior level of competence and productivity, not all of them will readily adapt to a new language, technology or development approach. Some developers will take to it quickly and do very well; others will have to work harder, but will eventually come up to speed; yet others may never become skilled even though they may not be aware of this fact.

When this happens, all the developers may be working hard, but the relative quality of their work — measured by adherence to best practices for the language/technology in question — can vary dramatically. This may not be apparent for some time, and when it does appear, it can be difficult to correct, from both a technical and organizational viewpoint.

Symptoms

Product architecture that must constantly be revised or refactored. Subsystems that are less stable than others or that are hard to integrate with the rest of the project. Developers consistently complaining about the quality or utility of a given developer’s code.

Consequences

Failure to achieve the expected benefits of the language/technology. Varying quality within the system under development; friction and even serious rifts between team members; project or product failure.

Detection

It’s a big help to have one or more team members who you know are skilled with the language/technology in question. That can be harder to determine than you think, even (or especially) if you think that you’re skilled with the language/technology as well. Process, design and/or code reviews may help to shine light on the problems.

Extraction

This can be one of the hardest issues for a manager, especially if the developer doesn’t recognize the deficiencies. The best solution is probably one of obvious or subtle mentoring: partnering that developer with someone of proven language/technology skills. This approach has two aims: to get the project back on track and to bring the developer’s skills up to speed.

Prevention

Seek the best developers. That may sound obvious, but a lot of companies are more interested in head count than talent and adaptability. The developer should be a self- starter and keenly interested in the language/technology in question, and should also have professional software engineering skills and the discipline to match.

Before the project even starts, spend time to train the developers, and promote the explicit understanding that their language/technology skills will be evaluated on a regular basis. Provide incentives for honesty and alternative assignments, such as building tools, for those whose skills lie elsewhere.

References

Webster, Pitfalls of Object-Oriented Development (1995).

Categories: managerial

That which gets measured gets accomplished or, at least, evaluated. That’s why various software metrics are used as an indication of progress and accomplishment. The best known and easiest to compute is lines of code (LOC), usually measured as thousands of lines of code (KLOC). Luckily, LOC has lost its appeal of the years because it encourages verbosity and constant additions to the code base, even though simplicity and refinement of existing code are ideal. Function points (FPs) are trendier, but they’re also harder to measure and so don’t get used as often.

The point is that these metrics and many others have little bearing on modern software engineering.

Use of the “wrong” metrics may in some circumstances be better than no metrics at all. In one project I worked on, we tracked the change in LOC on a subproject basis week by week; it gave a rough indication of activity in and stability of a subproject. But the wrong metric may turn out to be a bane if misused or misinterpreted by those above you.

Symptoms

Use of metrics that don’t appear to be meaningful or that, worse yet, are misleading. Misuse of metrics as a management club.

Consequences

Time is spent generating information that is of little value and that may give a misleading or erroneous indication of how the project is progressing. More seriously, developers may be encouraged, consciously or unconsciously, to “produce” in ways that run counter to the goals of the project.

Detection

Find out which metrics are being used (if any). Consider their influence and impact; see whether they help predict time or quality of development. Ask other people how they interpret these metrics.

Extraction

Abandon all irrelevant metrics and adopt appropriate ones. Educate all parties involved about what is being done and why. Establish benefits and rewards for those who take the time to use the metrics and whose development efforts improve as a result.

Prevention

From the outset, educate everyone — developers, technical managers, upper management — about what are the appropriate metrics and what they mean. Develop tools to automate metric generation as much as possible. Use the metrics regularly; distribute the results and discuss their meaning.

Be sure to automate these metrics as much as possible; trying to do these by hand will work once or twice but will not give you the constant feedback you need to make them truly useful. Case in point: Do you think anyone would use or continue to use the old “lines of code” metric if they had to count them by hand?

References

Webster, Bruce F. “Lies, Damned Lies, and Project Metrics” (Parts 1, 2, and 3). Baseline (online edition).

Comments posted by Gerald Weinberg at brucefwebster.com.

Categories: managerial

Self delusion and group delusion are all too common in software development projects. Several factors combine to bring this about. One is the natural optimism prevalent among software engineers, particularly when they are not allowed, encouraged, or required to spend sufficient time specifying and designing what they will develop. Another cause is the presumption that no major roadblocks or difficulties will be encountered along the way, either technical or organizational. A third factor is the persistent (if illogical) attitude on the part of upper management that because a project must be completed by a certain date, therefore it can and will be completed by that date, and information to the contrary is not welcome. Mix these and related factors and you have a major disaster in the making.

New technologies and/or methodologies (”TOMs”) have a way of magnifying these tendencies. Engineers become even more optimistic, technical managers see fewer roadblocks, and upper management, having read all the glowing articles about these particular TOMs in the business and trade publications, expects unrealistic results.

Symptoms

Irritation, anger, and disbelief when someone gives unpopular but honest appraisals of how things are going. Long, earnest explanations of why the standard rules of software development don’t apply in this case. And, of course, serious discrepancies between the planned schedule and actual results.

Consequences

Constant slips in the schedule, as expectations are continually reset to adjust to the new version of reality. Projects that ship very late, if at all. Loss of credibility for managers and developers. In the end, you end up looking dishonest, incompetent, or both.

Detection

Take aside the developers, managers, and others involved in the project, one at a time, and ask each person, “Do you think there’s anything we’re fooling ourselves about?” Make notes of the points each person brings up. Make a second pass through, but this time show them the list you’ve compiled and ask them what they agree with, what they disagree with, and what they’d like to add. The number and significance of items on the list should give you a good idea whether you and others are fooling yourselves about how the project is going.

Extraction

Given a significant list of items, you need to reschedule and plan again based on the information collected. This may not be easy or popular; in some cases, it may not even be possible, depending on how upper management reacts. Your choice may then be to either push ahead as best you can or find another job.

Prevention

Use the process described under Detection from the start of project planning and design. You may sacrifice popularity, but you’ll gain credibility—provided, of course, that they don’t reassign (or fire) you and put someone more amenable in charge.

References

[Professional experience]

Categories: managerial

What are the risks in modern software development? Look at the pitfalls listed in this book to start. Kind of makes you want to take up gardening, doesn’t it? On the other hand, being able to identify those risks and then manage them puts you way ahead in the development game and can make you extremely valuable as a technical manager.

Why do we sometimes fail to see and handle risks? There are various reasons. We don’t want to confront others and ask hard questions. We don’t want to expose our own ignorance. We keep hoping things will correct themselves. We don’t want to disappoint or upset those above us. We don’t want to have to fire anyone. We don’t want to appear not to trust others. We face resistance, forceful or subtle, as we try to point out these risks and deal with them.

Risk management is essential in modern software engineering. It’s essential for all the reasons it would be in any software development project. But it’s particularly essential if you are adopting a new technology or methodology (the “TOM”), because:

• the number of IT engineers that you have who are both skilled and experienced in the TOM is likely to be small;
• non-IT personnel, particularly upper management, are likely to have high expectations and mistaken perceptions regarding the TOM.

In short, adoption of a new TOM is likely to increase the risks of the project.

Symptoms

Closed door, closed eyes, closed mind. Constant trickle (or stream) of bad news from developers. Spending more time putting out fires and explaining problems to upper management than actively managing and completing tasks.

Consequences

Slipped schedules, missed milestones, project failures, lost jobs.

Detection

Ask everyone involved with the project what risks they think the project currently faces, with “risks” meaning anything that could cause the project to ship behind schedule, over budget, lacking specified features, in a form not acceptable to customers. If anything comes up you haven’t considered and which you aren’t handling, then you know you’ve fallen into this particular pit.

Extraction

Make an exhaustive list detailing all the previously unidentified and unhandled risks that you gleaned from the questioning in Detection. Distribute the list through the development group and then ask for any additions or corrections (a risk identified by one person may be handled by another). Come up with an assessment of each risk: how probable it is, how it could affect the project, and how it can be managed. Pass this list to upper management. Reset and reschedule the project, if necessary.

Prevention

Risk identification and management should be part of project planning and scheduling from the beginning. The more actively you work as a team to anticipate and handle risks, the fewer problems you’ll encounter. Be aware that this takes political courage, though; those above you don’t always want to hear what the risks are or why the project may not be completed in the time frame demanded.

References

Webster, from Pitfalls of Object-Oriented Development (1995).

Categories: managerial

This is a classic pitfall in software engineering. Typically, insufficient time is allocated for the problem specification, research, design, architecture, and review that should occur before coding and during each development cycle. Likewise, software quality assurance (SQA) is often given little time, money, or people. The whole focus is on coding, and often that is underestimated by assuming that development will be linear and that nothing will go wrong.

Adopting a new technology or methodology (the “TOM”) tends to compound this effect in several ways, some of which are discussed elsewhere. First, unrealistic expectations can creep in. Second, rapid prototyping and feature development can cause a false sense of progress. Third, coding time may well be genuinely reduced by the TOM.

As a result, an expectation can arise that all aspects of the development cycle, not just the coding portion, will be compressed. The first few times you use the TOM, it may require more time up front for design and architecture, and it’s likely to require as much or more testing time. As your development group gains more experience and expertise in the TOM, the entire cycle may start to compress, but that comes with time.

Symptoms

Non-coding tasks taking longer than the time allotted to them. Slowdowns during development due to a need to rethink design. Alpha and beta testing taking much longer than planned.

Consequences

Slipped schedules and missed deadlines. Rude surprises as design must be repeated or testing takes much longer than expected.

Detection

Apply Brooks’ rule of thumb: the time required for a project should break down into one-third for design and prototyping, one-sixth for implementing, and half for testing. If your proportions are radically different, then you may have misjudged the relative costs or at least, mislabeled them; a lot of design and testing gets buried inside implementation.

If you’re far along in your project, keep a close eye on time required for SQA and particularly for testing, which is usually underestimated. Agile methodologies do a better job of addressing testing up front, but SQA remains the poor cousin for most software projects.

Extraction

First, throw out your current schedule and do a hard reset of expectations, particularly among upper management. This is not easy to do, but honesty is always the best policy. Good luck.

Second, set up a development cycle—specify, design, prototype, review, implement, test—with the time allocated to each step in the cycle based roughly on the proportions given above. Recognize that the actual time for each step in each cycle will vary: An early cycle will tend to have more specification and design and less testing; a later cycle will reverse those proportions.

Third, manage through one complete cycle and see how well your estimated costs match reality. Adjust and repeat.

Prevention

First, get some project management software. It doesn’t have to be fancy; it just has to automate the task of adding the estimated times for each task, noting critical paths, and calculating a final date. This is important; attempting to schedule anything except the smallest and simplest project in your head will lead to unpleasant surprises.

Second, use steps two and three under Extraction to set up your schedule and to estimate relative costs. If possible, try this first with a relatively small project and then work up to larger projects. The goal is to be able to estimate both relative and absolute costs within a certain margin of error (say, 10 percent) on a regular basis.

References

Brooks, Frederick P., Jr. The Mythical Man-Month. Reading, MA: Addison-Wesley, 1995.

Webster, from Pitfalls of Object-Oriented Development (1995).

Well, now I’ve been hired by Ziff Davis Enterprises to write a weekly column on IT Management for the online version of Baseline. That column is titled “Surviving Complexity” and, again, draws upon work I’m doing for the book. My first column is up: “Lies, Damned Lies, and Metrics (Part I)”; here’s the opening paragraph:

When Capers Jones published Assessment and Control of Software Risks (Yourdon Press, 1994), he identified the most serious software risk in IT projects as “Inaccurate Metrics,” and the second most serious software risk as “Inadequate Measurement”. I remember being startled when I first read that back in 1995—they certainly weren’t what I would have chosen—and other authorities in the field criticized his choices. Yet, in the intervening years, I have moved closer and closer to Jones’ point of view.

Please feel free to check out the new column. Thanks. ..bruce..

Categories: managerial

The impulse to constantly add new and incremental features to a software program certainly isn’t unique to modern software develoment, or to a particular technology or methodology. It derives largely from three sources. Upper management and marketing want, and sometimes need, those additional features to win a customer, to better position the product against a competitor, or to better justify the expense of an in-house project. Engineers often find it far more fun, more interesting, and more rewarding to add or extend features than to make existing features work completely and perfectly. And customers supply a constant flow of demand for new and improved functionality.

The problem is that many modern technologies and methodologies intensify all these tendencies. With a decent software design and implementation, based on current technology, it can take literally only a few minutes to add or extend features. Frankly, that’s the payoff that lures many engineers to modern technologies in the first place: They get to do really neat stuff, do it quickly, and impress the boss while they’re at it. The boss, having seen how quickly engineers can extend or create functionality, doubles or trebles the list of must-have features. The technical manager, caught in the middle, wonders how to deal with all this.

Symptoms

Engineers focusing on adding new features instead of getting old ones working completely. Upper management passing down long (or even short) lists of additional features when the engineering schedule will be hard-pressed to accommodate the current ones. Customers failing to distinguish between what they’d like and what they’re willing to live with.

Consequences

Features that don’t work as completely or well as they were intended. Missed milestones and schedule slippage.

Detection

Review all existing features with the engineering team and get an honest assessment of where each feature is and what it will take to complete it. If necessary, do a design and code review for each feature. Compare the documented “essential features” list—and the schedule required to complete them—with the current feature set as well as those proposed by engineers and upper management, and find where the differences are coming in.

Extraction

Nothing fancy here: Get the absolute “drop dead” release date for the software and work backward from there, allocating time to design, implement, debug, and test each feature that is not yet completed. Show everyone (especially customers) the gap between the time and resources allocated those required for just the currently planned and requested features. Use whatever process works in your company for performing feature triage: keeping those that are absolutely essential, dropping those that can wait for the next release, and negotiating on the others.

Prevention

Use the process given in Extraction before a line of code is ever written. As features are proposed, collect them in a list, but do not allow any work on them. At intervals, repeat the process: You might have time (or need) to schedule some of the proposed features, but you may well have to drop previously required ones.

As with a prior pitfall on features, this pitfall is so an obvious and well-known that I’m almost embarrassed to include it here. Again, it is still so common that I must.

References

Webster, from Pitfalls of Object-Oriented Development (1995).