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Chapter 27: Miniature Milestones ” v: ‘’”‘’.’ CLASSIC MISTAKE CROSS-REFERENCE For more on the way that visibility Improves as a project progresses, see Section 8.1, The Software- Estimation Story.” CROSS-REFERENCE For more on recalibrating estimates, see”Recalibration” in Section 8.7. CROSS-REFERENCE For more on the hazards of too much schedule pressure,
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27.1 Using Miniature Milestones CLASSIC MISTAKE CROSS-REFERENCE For another example of this, see “Track schedule progress meticulously” in Section 16.2. Have developers create their own mini milestones. Some developers will view Miniature Milestones as micro-management, and, actually, they’ll be right. It is micro-management. More specifically, it’s micro project-tracking. However, not all micro-management is bad. The micro-management that developers resist is micro-management of the details of how they do their jobs. If you let people define their own miniature milestones, you allow them to control the details of their jobs. All you’re asking is that they tell you what the details are, which improves buy-in and avoids seeming like micro-management. Some people don’t understand the details of their jobs, and those people will feel threatened by this practice. If you handle their objections diplomatically, learning to work to a miniature-milestone schedule will serve as an educational experience for them. Keep milestones miniature. Make mini milestones that are achievable in 1 or 2 days. There’s nothing magical about this size limit, but it’s important that anyone who misses a milestone can catch up quickly. If people have done generally good jobs of estimating their work, they should be able to catch up on any particular missed milestone by working overtime for 1 or 2 days. Another reason to keep milestones small is to reduce the number of places that unforeseen work can hide. Developers tend to view a week or weekend as an infinite amount of time they can accomplish anything. They don’t think about exactly what’s involved in creating the “data conversion module,” and that’s .why the job takes 2 weeks instead of the estimated one weekend. But most developers won’t commit to tackling a problem in 1 or 2 days unless they understand what it involves. To be sure you’re basing your schedule on meaningful estimates, insist on further decomposingtasksthat areabove the “infinite amount oftime” threshold for your environment. Make milestones binary. Define milestones so that they are either done or not. The only two statuses are “done” and “not done.” Percentages are not used. As soon as people are allowed to report that they are “90 percent done,” the milestones lose their ability to contribute to a clear view of project progress. Some people can’t resist the temptation to fudge their status reporting with Miniature Milestones. “Are you done?” you ask. “Sure!” they say. “Are you 100 percent done?” you ask. “Well, uh, I’m 99 percent done!” they say. “What do you mean, ‘99 percent done?” you ask. And they say, “Uh, I mean that I still need to compile and test and debug the module, but I’ve got it written!” Be fanatic about interpreting milestones strictly.
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Chapter 27: Miniature Milestones 27.1 Using Miniature Milestones CROSS-REFERENCE For more on initiating new measures in response to a crisis, see “Timing” in Section 16.2. You can apply the Miniature Milestones practice throughout the life of a project. You can apply it to early activities such as Requirements Specification and Evolutionary Prototyping; in fact, it is particularly useful in focusing those hard-to-direct activities. For maximum benefit, the Miniature Milestones practice will be implemented at the project level by the technical lead or manager, whichever is appropriate. But individual contributors can implement it on a personal level even if their leaders don’t. The amount of detail required when implementing Miniature Milestones will give pause to whoever has responsibility for tracking those details, especially on large projects. But large projects are the projects that most commonly spin, out of control, and it is on those projects that this kind of detailed tracking is especially needed. Initiate Miniature Milestones early or in response to a crisis. Miniature Milestones provide a high degree of project control. Set them up early in the project or in response to an acknowledged crisis. If you set them up at other times, you am the risk of seeming Draconian. As with other aspects of project control, it’s easier to overcontrol in the beginning and relax control as the project progresses than it is the other way around. As Barry Bbehm and Rony Ross say, “Hard-soft works better than soft-hard” (Boehm and Ross 1989).
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How does a project get to be a year late? One day at a time. Frederick P. Brooks, Jr. CROSS-REFERENCE For more on developer motivations, see Chapter 11, “Motivation.” CROSS-REFERENCE For more on detailed estimation, see “Estimate at a low level of detail” in Section 8.3. realistic. Since your milestones are fine-grained, you will find out early that you have a problem. That gives you an early opportunity to recalibrate your schedule, adjust your plan, and move on. . Fine-grain control. In Roger’s Version,John Updike describes a diet plan in which a woman weighs herself every Monday morning. She is a small woman, and she wants to weigh less than 100 pounds. If on Monday morning she finds that she weighs more than 100 pounds, she eats only carrots and celery until she again weighs less than 100 pounds. She reasons that she can’t gain more than 1 or 2 pounds in a week, and if she doesn’t gain more than 1 or 2 pounds, she certainly won’t gain 10 or 20. With her approach, her weight will always stay close to where she wants it to be. The Miniature Milestone practice applies this same idea to software development, and it’s based on the idea that if your project never gets behind schedule by more than a day or so, it is logically impossible for it to get behind schedule by a week or a month or more. Milestones also help to keep people on track. Without short-term milestones, it is too easy to lose sight of the big picture. People spend time on detours that seem interesting or productive in some vague sense but that fail to move the project forward. With larger-grain tracking, developers get off schedule by a few days or a week, and they stop paying attention to it. With Miniature Milestones, everyone has to meet their targets every day or two. If you meet most of your milestones just by working a full day and meet the rest by working an extra full day you will meet the overall, big milestones as well as the little ones. There’s no opportunity for error to creep in. Improved motivation. Achievement is the strongest motivator for software developers, and anything that supports achievement or makes progress more palpable will improve motivation. Miniature Milestones make progress exceptionally tangible. Reduced schedule risk. One of the best ways to reduce schedule risk is to break large, poorly defined tasks into smaller ones. When creating an estimate, developers and managers tend to concentrate on the tasks they understand best and to shrug off the tasks they understand least. The frequent result is that a 1-week “DBMS interface” job can turn out to take an unexpected 6 weeks because no one ever looked at the job carefully. Miniature Milestones address the risk by eliminating large schedule blobs entirely.
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Chapter 27: Miniature Milestones ‘ CLASSIC MISTAKE Imagine that you’re a pioneer heading from the east coast to the west. Your journey is much too long to be completed in a single day, so you define a set of points that will mark the significant milestones on your journey. It’s a 2500 mile journey, so you mark five milestones, each about 500 miles apart. Major milestones 500 miles apart are great for setting long-term direction, but they are lousy for figuring out where to go each day especially when you’re traveling only, say, 25 miles per day. For that, you need finer-grain control. If you know that your big milestone is 500 miles away, north-by-northwest, you can take a compass reading, find a closer landmark that’s roughly north- by-northwest, and then strike out toward that. Once you reach that closer landmark, you take another compass reading, find another landmark, and strike out again. The close landmarks that you pick the tree, rock formation, river, or hilltop serve as your miniature milestones. Reaching the miniature milestones provides you with a steady sense of accomplishment. Since you pick only milestones that are between you and your next big milestone, reaching the miniature milestone also gives you confidence that you will eventually reach your larger objective. Miniature Milestones’ support for rapid development boils down to four factors: improved status visibility, fine-grain control, improved motivation, and reduced schedule risk. Improved status visibility. One of the most common problems on software- development projects is that neither developers, project leaders, managers, nor customers are able to assess the project’s status accurately. Say nothing about whether they can predict when the project will be done, they don’t even know how much they’ve already completed! Jim McCarthy cautions against letting a developer “go dark” (McCarthy 1995a). You believe that everything’s going along OK. Why? Because every day you ask your developers, “How’s it going?” They say, “Fine.” And then one day you ask, “How’s it going?” And they say, “Urn, we’re going to be about 6 months late.” Wow! They slipped 6 months in 1 day! How did that happen? It happened because they were “working in the dark” neither you nor they had enough light on their work to know that they had been slipping all along. With Miniature Milestones, you define a set of targets that you have to meet on a near-daily basis. If you start missing milestones, your schedule isn’t
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Miniature Milestones The Miniature Milestones practice is a fine-grain approach to project tracking and control that provides exceptional visibility into a project’s status. It produces its rapid-development benefit by virtually eliminating the risk of uncontrolled, undetected schedule slippage. It can be used on business, shrink-wrap, and systems software projects, and it can be used throughout the development cycle. Keys to success include overcoming resistance of the people whose work will be managed with the practice and staying true to the practice’s “miniature” nature. Efficacy Potential reduction from nominal schedule: Improvement in progress visibility: Effect on schedule risk: Chance of first-time success: Chance of long-term success: Fair Very Good Decreased Risk Good Excellent Major Risks None Major Interactions and Trade-Offs Especially well-suited to project recovery Especially effective when combined with the Daily Build and Smoke Test practice Works well with Evolutionary Prototyping, User-Interface Prototyping, Requirements Specification, and other hard-to-manage project activities Trades increase in project-tracking effort for much greater status visibility and control 481
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Further Reading | Grady, Robert B. Practical Software Metrics for Project Management and Process Improvement. Englewood Cliffs, N.J.: PTR Prentice Hall, 1992. This book is the follow-on to Grady and CaswelTs earlier book and extends the discussion of lessons learned at Hewlett-Packard, It contains a particularly nice presentation of a set of software business- management graphs, each of which is annotated with the goals and questions that the graph was developed in response to. Jones, Capers. Applied Software Measurement: Assuring Productivity and Quality. New York: McGraw-Hill, 1991. This book contains Jones’s recommendations for setting up an organization-wide measurement program. It is a good source of information on functional metrics (the alternative to lines-of-code metrics). It describes problems of measuring software, various approaches to measurement, and the mechanics of building a measurement baseline. It also contains excellent general discussions of the factors that contribute to quality and productivity. Conte, S. D., H. E. Dunsmore, and V. Y. Shen. Software Engineering Metrics and Models. Menlo Park, Calif.: Benjamin/Cummings, 1986. This book catalogs software-measurement knowledge, including commonly used measurements, experimental techniques, and criteria for evaluating experimental results. It is a useful, complementary reference to either of Grady’s books or to Jones’s book. IEEE Software, July 1994. This issue focuses on measurement-based process improvement. The issue contains articles that discuss the various process- rating scales and industrial experience reports in measurement- based process improvement. 479
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: I Chapter 26: Measurement 26.5 26.6 The Bottom Line on Measurement Measurement programs naturally have some of the best data available to support their efficacy. Metrics guru Capers Jones reports that organizations that have established full software-measurement programs have often improved quality by about 40 percent per year and productivity by about 15 percent per year for 4 to 5 years consecutively (Jones 1991, 1994). He points out that only a handful of U.S. organizations currently have accurate measures of software defect rates and defect removal and that those organizations tend to dominate their industries (Jones 1991). The cost for this level of improvement is typically from 4 to 5 percent of the total software budget. Keys to Success in Using Measurement Here are the keys to success in using Measurement: Set up a measurement group. Put it in charge of identifying useful measurements and helping projects to measure themselves. Track time-accounting data at a fine level of granularity. Start with a small set of measurements. Select what you want to measure by using the Goals, Questions, Metrics approach. Don’t just collect the data. Analyze it and provide feedback about it to the people whose work it describes. Further Reading Software Measurement Guidebook. Document number SEL-94-002. Greenbelt, Md.: Goddard Space Flight Center, NASA, 1994. This is an excellent introductory book that describes the basics of how and why to establish a measurement program. Among other highlights, it includes a chapter of experience-based guidelines, lots of sample data from NASA projects, and an extensive set of sample data-collection forms. You can obtain a single copy for free by writing to Software Engineering Branch, Code 552, Goddard Space Flight Center, Greenbelt, Maryland 20771. Grady, Robert B., and Deborah L. Caswell. Software Metrics: Establishing a Company-Wide Program. Englewood Cliffs, N.J.: Prentice Hall, 1987. Grady and Caswell describe their experiences in establishing a softwaremetrics program at Hewlett-Packard and how to establish one in your organization.
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FURTHER READING For an excellent discussion of problems with lines-ofcode measurements, see Programming Productivity (Jones 1986a). 26.3 26.4 26.4 Measurement’s Interactions with Other Practices Misleading information from lines-of-code measurements. Most measurement programs will measure code size in lines of code, and there are some anomalies with that measurement. Here are some of them: Productivity measurements based on lines of code can make high-level languages look less productive than they are. High-level languages implement more functionality per line of code than low-level languages. A developer might write fewer lines of code per month in a high-level language and still accomplish far more than would be possible with more lines of code in a Iow4evel language. Quality measurements based on lines of code can make high-level languages look as if they promote lower quality than they do. Suppose you have two equivalent applications with the same number of defects, one written in a high-level language and one in a low-level language. To the end-user, the applications will appear to have exactly the same quality levels. But the one written in the low-level language will have fewer defects per line of code simply because the lower-level language requires more code to implement the same functionality. The fact that one application has fewer defects per line of code creates a misleading impression about the applications’ quality levels. To avoid such problems, beware of anomalies in comparing metrics across different programming languages. Smarter, quickerways ofdoingthingsmay result in less code. Also consider using function points for some measurements. They provide a universal language that is better suited for some kinds of productivity and quality measurements. Side Effects of Measurement The main side effect of a measurement program is that what you measure gets optimized. Depending on what you measure, you might end up optimizing defect rates, usability, execution efficiency, schedule, or some other factor. Measurement’s Interactions with Other Practices A measurement program provides the foundation for improvement in areas including estimation (Chapter , scheduling (Chapter 9), and productivity- tool evaluation (Chapter 15). Although it is possible to design a measurement program so that it undercuts a rapid-development project, there is no reason that a well-designed measurement program should interact negatively with any other practice.
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Chapter 26: Measurement 26.2 CLASSIC MISTAKE Data accuracy. The fact that you measure something doesn’t mean the measurement is accurate. Measurements of the software process can contain a lot of error. Sources of errors include unpaid and unrecorded overtime, charging time to the wrong project, unrecorded user effort, unrecorded management effort, unrecorded specialist effort on projects, unreported defects, unrecorded effort spent prior to activating the project-tracking system, and inclusion of non-project tasks. Capers Jones reports that most corporate tracking systems tend to omit 30 to 70 percent of the real effort on a software project (Jones 1991). Keep these sources of error in mind as you design your measurement program. Managing the Risks of Measurement In general, Measurement is an effective risk-reduction practice. The more you measure, the fewer places there are for risks to hide. Measurement, however, has risks of its own. Here are a few specific problems to watch for. Over-optimization of single-factor measurements. What you measure gets optimized, and that means you need to be careful when you define what to measure. If you measure only lines of code produced, some developers will alter their coding style to be more verbose. Some will completely forget about code quality and focus only on quantity. If you measure only defects, you might find that development speed drops through the floor. It’s risky to try to use too many measurements when you’re setting up a new measurement program, but it’s also risky not to measure enough of the project’s key characteristics. Be sure to set up enough different measurements that the team doesn’t overoptimize for just one. Measurements misused for employee evaluations. Measurement can be a loaded subject. Many people have had bad experiences with measurement in SAT scores, school grades, work performance evaluations, and so on. A tempting mistake to make with a software-measurement program is to use it to evaluate specific people. A successful measurement program depends on the buy-in of the people whose work is being measured, and it’s important that a measurement program track projects, not specific people. Perry, Staudenmayer, and Votta set up a software research project that illustrated exemplary use of measurement data. They entered all data under an ID code known only to them. They gave each person being measured a “bill of rights,” including the right to temporarily discontinue being measured at any time, to withdraw from the measurement program entirely, to examine the measurement data, and to ask the measurement group not to record something. They reported that not one of their research subjects exercised these rights, but it made their subjects more comfortable knowing they were there (Perry, Staudenmayer; and Votta 1994).
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