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A distillation of Robert Glass’s lifetime experience
Robert Glass is a software engineering developer, manager, researcher and author who, until six months ago, I had vaguely heard of; somehow I had missed reading any of his 25 books. After seeing citations to some of Glass’s books, I bought half-a-dozen or so, second hand. They are well written, and twenty-five years ago I would have found them very interesting; now I simply agree with the points made.
“software creativity 2.0” is Glass’s penultimate book, published in 2006, and the one that caught my attention. I would recommend his other books to anybody who is new to software engineering, or experienced people looking for an encapsulation in print of what they encounter at work.
Glass was 74 when this book was published, having started working in computing in 1954. He was there and seems to have met many of the major names in software engineering, working with some of them.
The book is a clear-eyed summary of what Glass has learned from being involved with software engineering, and watching method/tool fashions come and go. My favourite section draws parallels between software development cultures and the culture of Rome vs. Greek vs. Barbarian:
Models Roman Greek Barbarians Organization Organize people Organize things Barely Focus Manages projects Writes programs Leap to coding Motivation Group goals Problem to be solved Heroics Working style Organizations Small groups Solo Politics Imperial Democratic Anarchist Tool use People are tools Things are tools Avoid tools Status Function-ocracy Meritocracy Fear-ocracy Activities Plan things Do things Break things Emphasize Form Substance Line of code |
The contents are essentially a collection of short essays, organized under the 19 chapter headings below, which in turn are grouped into four parts. The first nine chapters (part I, and 60% of pages) contain the experience based material, with the subsequent parts/pages having a creativity theme. A thread running through the discussion is idealism/practice:
Discipline vs. Flexibility
Formal methods versus Heuristics
Optimizing versus Satisficing
Quantitative versus Qualitative Reasoning
Process versus Product
Intellect vs. Clerical Tasks
Theory vs. Practice
Industry vs. Academe
Fun versus Getting Serious
Creativity in the Software Organization
Creativity in Software Technology
Creative Milestones in Software History
Organizational Creativity
The Creative Person
Computer Support for Creativity
Creativity Paradoxes#'twas Always Thus
A Synergistic Conclusion
Other Conclusions |
This book deserves to be widely read. I found it best to read a single section per sitting.
Rereading The Mythical Man-Month
The book The Mythical Man-Month by Fred Brooks, first published in 1975, continues to be widely referenced, my 1995 edition cites over 250K copies in print. In the past I have found it to be a pleasant, relatively content free, read.
Having spent some time analyzing computing data from the 1960s, I thought it would be interesting to reread Brooks in the light of what I had recently learned. I cannot remember when I last read the book, and only keep a copy to be able to check when others cite it as a source.
Each of the 15 chapters, in the 1975 edition, takes the form of a short five/six page management briefing on some project related topic; chapters start with a picture of some work or art on one page, and a short quote from a famous source occupies the opposite page. The 20th anniversary edition adds four chapters, two of which ‘refire’ Brooks’ 1986 paper introducing the term No Silver Bullet (that no single technlogy will produce an order of magnitude improvement in productivity).
Rereading, I found the 1975 contents to be sufficiently non-specific that my newly acquired knowledge did not change anything. It was a pleasant read, various ideas and some data points are presented, the work of others is covered and cited, a few points are briefly summarised and the chapter ends. The added chapters have a different character than the earlier ones, being more detailed in their discussion and more specific in suggesting outcomes. The ‘No Silver Bullet’ material dismisses some of the various claimed discoveries of a silver bullet.
Why did the book sell so well?
The material is an easy read, and given that no solutions are heavily pushed, there is little to disagree with.
Being involved on a project for the first time can be a confusing experience, and even more experienced people get lost. Brooks can provide solace through his calm presentation of project behaviors as stuff that happens.
What project experience did Brooks have?
Brooks’ PhD thesis The Analytic Design of Automatic Data Processing Systems was completed in 1956, and, aged 25, he joined IBM that year. He was project manager for System/360 from its inception in late 1961 to its launch in April 1964. He managed the development of the operating system OS/360 from February 1964, for a year, before leaving to found the computer science department at the University of North Carolina at Chapel Hill, where he remained.
So Brooks gained a few years of hands-on experience at the start of his career and spent the rest of his life talking about it. A not uncommon career path.
Managing the development of an O/S intended to control a machine containing 16K of memory (i.e., IBM’s System/360 model 30) might not seem like a big deal. Teams of half-a-dozen good people had implemented projects like this since the 1950s. However, large companies create large teams, operating over multiple sites, with every changing requirements and interfaces, changing hardware, all with added input from marketing (IBM was/is a sales-driven organization). I suspect that the actual coding effort was a rounding error, compared to the time spent in meetings and on telephone calls.
Brooks looked after the management, and Gene Amdahl looked after the technical stuff (for lots of details see IBM’s 360 and early 370 systems by Pugh, Johnson, and Palmer).
Brooks was obviously a very capable manager. Did the O/360 project burn him out?
Looking for a measurable impact from developer social learning
Almost everything you know was discovered/invented by other people. Social learning (i.e., learning from others) is the process of acquiring skills by observing others (teaching is explicit formalised sharing of skills). Social learning provides a mechanism for skills to spread through a population. An alternative to social learning is learning by personal trial and error.
When working within an ecosystem that changes slowly, it is more cost-effective to learn from others than learn through trial and error (assuming that experienced people are available to learn from, and the learner is capable of identifying them); “Social Learning” by Hoppitt and Layland analyzes the costs and benefits of using social learning.
Since its inception, much of software engineering has been constantly changing. In a rapidly changing ecosystem, the experience of established members may suggest possible solutions that do not deliver the expected results in a changed world, i.e., social learning may not be a cost-effective way of building a skill set applicable within the new ecosystem.
Opportunities for social learning occur wherever developers tend to congregate.
When I started writing software people, developers would print out a copy of their code to take away and correct/improve/add-to (this was when 100+ people were time-sharing on a computer with 256K words of memory, running at 1 MHz). People would cluster around the printer, which ran sufficiently slowly that it was possible, in real-time, to read the code and figure out what was going on; it was possible to learn from others code (pointing out mistakes in programs that people planned to hand in was not appreciated). Then personal computers became available, along with low-cost printers (e.g., dot matrix), which were often shared, and did not print so fast that an experienced developer could not figure things out in real-time. Then laser printers came along, delivering a page at a time every 15 seconds, or so; experiencing the first print out from a Laser printer, I immediately knew that real-time code reading was a thing of the past (also, around this time, full-screen editors achieved the responsiveness needed to enthral developers, paper code listings could not compete). A regular opportunity for social learning disappeared.
Mentoring and retrospectives are intended as explicit (perhaps semi-taught) learning contexts, in which social learning opportunities may be available.
The effectiveness of social learning is dependent on being able to select a good enough source of expertise to learn from. Choosing the person with the highest prestige is a common social selection technique; selecting web pages appearing on the first page of a Google search is actually a form of conformist learning (i.e., selecting what others have chosen).
It is possible to point at particular instances of social learning in software engineering, but to what extent does social learning, other than explicit teaching, contribute to developer skills?
Answering this question requires enumerating all the non-explicitly taught skills a developer uses to get the job done, excluding the non-developer specific skills. A daunting task.
Is it even possible to consistently distinguish between social learning (implicit or taught) and individual learning?
For instance, take source code indentation. Any initial social learning is likely to have been subsequently strongly influenced by peer pressure, and default IDE settings.
Pronunciation of operator names is a personal choice that may only ever exist within a developer’s head. In my head, I pronounce the ^ operator as up-arrow, because I first encountered its use in the book Algorithms + Data Structures = Programs which used the symbol ↑, which appears as the ^ character on modern keyboards. I often hear others using the word caret, which I have to mentally switch over to using. People who teach themselves to program have to invent names for unfamiliar symbols, until they hear somebody speaking code (the widespread availability of teach-yourself videos will make it rare to need for this kind of individual learning; individual learning is giving way to social learning).
The problem with attempting to model social learning is that much of the activity occurs in private, and is not recorded.
One public source of prestigious experience is Stack Overflow. Code snippets included as part of an answer on Stack Overflow appear in around 1.8% of Github repositories. However, is the use of this code social learning or conformist transmission (i.e., copy and paste)?
Explaining social learning to people is all well and good, but having to hand wave when asked for a data-driven example is not good. Suggestions welcome.
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