Reflections on Software Engineering

Van Halen may have known more about project management than most program managers. Van Halen’s legendary “No Brown M&Ms Rider” is simultaneously the greatest example of rock star excess and project signaling I’ve ever seen. As David Lee Roth puts it:

The contract rider read like a version of the Chinese Yellow Pages because there was so much equipment, and so many human beings to make it function. So just as a little test, in the technical aspect of the rider, it would say “Article 148: There will be fifteen amperage voltage sockets at twenty-foot spaces, evenly, providing nineteen amperes . . .” This kind of thing. And article number 126, in the middle of nowhere, was: “There will be no brown M&M’s in the backstage area, upon pain of forfeiture of the show, with full compensation.”

So, when I would walk backstage, if I saw a brown M&M in that bowl . . . well, line-check the entire production. Guaranteed you’re going to arrive at a technical error. They didn’t read the contract. Guaranteed you’d run into a problem. Sometimes it would threaten to just destroy the whole show. Something like, literally, life-threatening.

In economics, signals are indicators that convey specific meaning between producers and consumers. For example, when you see THX on the side of a set of speakers, you know the speakers are going to probably be of audiophile quality. The THX logo is the speaker manufacturer’s signal to you, the consumer, that these speakers are really good. To David Lee Roth and the Van Halen road crew, the presence of brown M&Ms indicated that the hosting venue had not understood all details of the contract and had very likely made a mistake in configuring the set. One mistake in this case could cause malfunctions during the show or even the death of a crew member.

As it turns out, signaling software projects isn’t that difficult. The 12 step Joel Test is a reasonable signal for software development companies. While the Joel Test is nice for getting a feel for a company before you work for them, the concept is still useful once you’ve got the job and the project is in full swing.

Ultimately signals, also known as tripwires or triggers, are really just binary metrics for uncovering potential problems your project might be facing before the problems explode in your hands. When some condition is met (the signal), you know it has specific significance and prompts certain actions to prevent a problem from occurring. Triggers are most often used with risk management but their use should not be exclusive to that practice. In fact, if you’re collecting real data, you have even more opportunities for identifying signals outside of risk management.

On past projects I’ve used signals for a variety of issues. Here are some examples.

• During the past 3 iterations the team identified between 15 and 20 defects. I expect a similar number of defects to be detected for this iteration. If more defects are detected, there may be a disconnection in understanding between requirements, design, and implementation. If fewer defects are detected, tests may not have been as rigorously defined as they should have been.
• A Fagan inspection completed in less than one hour with a rate of 400 LOC/hour. Since most inspections have covered only 250 LOC/hour it is likely that this inspection was not effective and the results not reliable since the inspection team sped through the code.
• When evaluating potential open source libraries, Source Forge projects without a website shows a general lack of dedication to the project and indicates that the software is probably of poor quality or ill-maintained; the library is worth neither the time nor effort to use.
• Tasks that have been estimated to require longer than 9 hours have probably not been thoroughly thought through.
• No risks have been identified for this project or risks have not been updated for several iterations. This implies that the team doesn’t have a realistic understanding of what problems the project faces.

In each of these examples, when the signal is heard, I knew there was going to be a problem on the project.

Work with your team to establish signals for your project. The best part is that once you’ve decided on the signals for your team, when triggers are tripped you can throw a Van Halen sized rock star fits in your cubicle! Well, try to resist throwing your monitor out the window anyway.

Software developers are, in their heart of hearts, dataphiles – people who are absolutely in love with data. When was the last time you had a passionate discussion about frame rates, hardware benchmarks, gadget specs, sports statistics, dungeons and dragons, the merits of high def…the list goes on. Face it, you love data. You love comparing things using data. You don’t feel comfortable making decisions without a comprehensive comparison of data.

Why then do most software developers treat software development differently?

Tom DeMarco recently brought his own famous quote into question (pdf), musing that not only is it possible to control what you can’t measure, but the most important stuff you need to control on a software project is impossible to measure. Once again, DeMarco is wrong (in my opinion anyway).

Wikipedia is one of the most controlled projects on the planet

One the surface, Wikipedia is the Wild West of online content. Not only can anyone edit any page, but content from Wikipedia is widely proliferated in the media and (sadly) school reports. Wikipedia is the single greatest success of user generated content in the history of mankind (”The Internet,” as the medium, doesn’t count). What started with a dozen humble articles has evolved into the most comprehensive encyclopedia ever created and includes everything from the fundamentals of science to the definitive source on Babylon 5.

What folks seem to forget is that even in the Wild West, there were laws and there were lawmen. Though we love to think romantically about such brigands and gunslingers as Jesse James, Billy the Kid, and Butch Cassidy, most stories about these historic figures are greatly exaggerated. So too is the case with Wikipedia.

Let’s take a closer look at the Wikipedia entry for Billy the Kid. This article belongs to a number of internal WikiProjects, visible from the top of the article’s talk page. The WikiProject Biography is not unlike most projects in Wikipedia. There defined processes for assessing articles, conducting peer reviews. There are rubrics defined for assessing the quality of articles within the project. People even take on specific roles and responsibilities within the project. The collection of processes and information serves as the main means of coordination for content contributors and helps the group control articles within the scope of the project.

The WikiProject Biography even collects metrics on articles which it then uses to make decisions concerning the articles under the project. The metrics are derived from quantifiable data and help control the project.

As it turns out, Wikipedia is not the lawless territory of the internet it has been made out to be.

You can measure the immeasurable

Wikipedia works because people were able to figure out ways to measure things that usually can’t be measured. The fundamental principle that many people overlook is that binary is a metric too. Yes or no questions can be just as effective a measure as any complex metric. Did everyone fill out their task data today? Yes or no. Did the estimate match the actual? Yes or no. Did the test pass? Yes or no. Is the project done? Yes or no. Have we identified risks? Yes or no. Has this risk become a problem? Yes or no.

At the heart of every complicated metric is really a series of yes or no, binary questions. When considering whether the project is done, you have to define done. One way of defining done is in terms of a checklist. Is feature 1 done? Is feature 2 done? Defining done for a feature could be as simple as checking whether all the tests have passed for the feature, again a binary measure.

For more subjective assessments, you can rely on observation-based, experience-defined rubrics. Does the team get along with one another? In the simplest form, this could be a binary metric (Am I friends with everyone on the team?) but it could also be more complicated relying on gut feelings and a guiding rubric (”we never hang out together and don’t trust one another” might indicate low harmony while “we hang out often and feel comfortable sharing personal stories” could indicate high harmony). Teachers use rubrics and experience to judge subjective assignments everyday. The difference is that they slap a grade on it and send it home as a report card.

While DeMarco is correct that many of most critical things in a project are the most difficult to measure, it is possible to create measurements if you feel it is important enough to do so. How would you assess whether you have a good architecture that solves the problem at hand? Rubrics might play a part but so too might binary gates based on quality attribute scenarios or intricate observations concerning design trends over time. If you think hard enough, you’ll find that it’s extremely easy to find measuring points for nearly every aspect of a software project.

Whatever you do, don’t become a mindless, data-driven robot

I love data and I know you do to. While it’s tempting to inject data collection and derive metrics for every aspect of a project (because it’s fun and informative!) don’t. Collecting data and calculating metrics can be expensive. Not so expensive that you shouldn’t use it, but expensive enough so that you shouldn’t use it on everything. I like to compare using metrics to eating out at restaurants. Once or twice a week isn’t that big a deal, but it’s not something you should do every day if you’re trying to watch your budget.

DeMarco is right about one thing: control is not the end-all-be-all of software engineering. Consider carefully, what are the most risky parts of my project? What are the parts of my project that even require control? What are the parts in which I need more insight or want to improve? Strategically develop metrics for these areas and don’t worry about measuring the rest. Trust me, the world won’t end. If you don’t know what you’re doing, start with a simple binary measure. And above all, if something isn’t working, change it.