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Programming productivity refers to a variety of software development issues and methodologies affecting the quantity and quality of code produced by an individual or team. Key topics in productivity discussions have included:
- Amount of code that can be created or maintained per programmer (often measured in source lines of code per day)
- Detecting and avoiding errors (through techniques like Agile Software Development, six sigma management, zero defects coding, and Total Quality Management)
- Software cost estimation (cost being a direct consequence of productivity)
The relative importance of programming productivity has waxed and waned along with other industry factors, such as:
- The relative costs of manpower versus machine
- a substantially less expensive global workforce is available via the Internet
- The size and complexity of the systems being built
- Highly publicized projects that suffered from delays or quality problems
- Development of new technologies and methods intended to address productivity issues
- Quality management techniques and standards
- apathy may be a factor (productivity needs to be a goal)
A generally accepted working definition of programmer productivity needs to be established and agreed upon. Appropriate metrics need to established. Productivity needs to be viewed over the lifetime of code. Example: Programmer A writes code in a shorter interval than programmer B but programmer A's code is of lower quality and months later requires additional effort to match the quality of programmer B's code; in such a case, it is fair to claim that programmer B was actually more productive.
Hardware aspects of programmer productivity
It is unfair to measure programmer productivity without factoring in the software and hardware tools that have been provided to the programmers being measured. Example: a programmer with two displays is likely to be more productive than a programmer with a single display. With solid state drives becoming less expensive, one's hardware can be fine tuned for faster compilation as is required by new development paradigms such as TDD (test driven development).
An extensive literature exists dealing with such issues as software productivity measurement, defect avoidance and removal, and software cost estimation. The heyday of such work was during the 1960s-1980s, when huge mainframe development projects often ran badly behind schedule and over budget. A potpourri of development methodologies and software development tools were promulgated, often championed by independent consultants brought in as troubleshooters on critical projects. The U.S. Department of Defense was responsible for much research and development in this area, as software productivity directly affected large military procurements.
In those days, large development projects were generally clean-sheet implementation of entire systems, often including their own system-level components (such as data management engines and terminal control systems). As a result, large organizations had enormous data processing staffs, with hundreds or thousands of programmers working in assembly language, COBOL, JOVIAL, Ada, or other tools of the day.
Modern computer use relies much more heavily on the use of standardized platforms and products, such as the many general-purpose tools available today under Linux and the Microsoft operating systems. Organizations have more off-the-shelf solutions available, and computer use is a basic job requirement for most professionals. Tasks that once would have required a small development team are now tackled by a college intern using Microsoft Excel. The result has been a trend toward smaller IT staffs and smaller development projects. With larger projects, techniques like rapid prototyping have shortened development project timelines, placing a priority on quick results with iterative refinement. Traditional programming-in-the-large has thus become rare – the domain of industry giants like Microsoft and IBM. As a result, although programming productivity is still considered important, it is viewed more along the lines of engineering best practices and general quality management, rather than as a distinct discipline.
A need for greater programmer productivity was the impetus for categorical shifts in programming paradigms. These came from
- Speed of code generation
- Approach to maintenance
- Emerging technologies
- Learning curve (training required)
- Approach to testing
- Software Cost Estimation with Cocomo II, Barry W. Boehm et al., Prentice Hall, 2000. ISBN 978-0-13-026692-7.
- Developing Products in Half the Time: New Rules, New Tools, Preston G. Smith and Donald G. Reinertsen, Wiley, 1997. ISBN 978-0-471-29252-4
- Programming Productivity, Capers Jones, Mcgraw-Hill, 1986. ISBN 978-0-07-032811-2
- Estimating Software Costs, Capers Jones, McGraw-Hill, 2007. ISBN 978-0-07-148300-1
- "Coding Horror: Joining The Prestigious Three Monitor Club" (December 2006) http://www.codinghorror.com/blog/2006/12/joining-the-prestigious-three-monitor-club.html
- "Coding Horror: The Programmer's Bill of Rights" (August 2006) http://www.codinghorror.com/blog/2006/08/the-programmers-bill-of-rights.html
- "Dual Monitors", Bob Rankin http://askbobrankin.com/dual_monitors.html
"... studies I've read come to the same conclusion: having a dual monitor in a workplace setting can increase productivity by 20 to 50 percent. If you're a computer programmer, it should be obvious that having your source code on one side and your program on the other side of a dual monitor display would be very helpful. Other areas where a dual monitor setup are helpful include customer service reps, web design, and creation of newsletters or PowerPoint presentations."