Software Developer Productivity Measure
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A Software Developer Productivity Measure is a worker productivity measure for a software developer.
- Context:
- It can range from being an Individual Software Developer Output Measure to being an Aggregate Software Developer Output Measure.
- It can range from being a Software Developer Output per Hour Measure, Software Developer Output per Hour Day, Software Developer Output per Hour Week, Software Developer Output per Year Measure, ...
- It can be associated to a Software Developer Output Growth Rate.
- It can range from being an Individual Software Developer Productivity Measure to being an Aggregate Software Developer Productivity Measure.
- ...
- Example(s):
- Counter-Example(s):
- See: Job Enthusiasm, Peer Support, Useful Feedback.
References
2023
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- A Software Developer Productivity Measure is a metric or set of metrics used to evaluate the efficiency, effectiveness, and overall performance of a software developer or a development team. These measures help organizations optimize their software development processes, allocate resources, and assess the progress of projects. They can also be used to identify areas of improvement and to motivate developers.
- There are several different productivity measures, each with their own advantages and limitations. Some examples include:
- Lines of code (LOC): This metric measures the number of lines of code written by a developer or a team. It is a simple and widely-used measure, but it can be misleading, as it doesn't account for code quality, complexity, or efficiency.
- Function points (FP): Function points are a measure of the functionality provided by a software system, considering factors such as inputs, outputs, user interactions, and data processing. This metric helps assess the value of the developed software, but it requires a deep understanding of the application domain and can be challenging to calculate.
- Code quality metrics: These metrics focus on evaluating the quality of the code produced by a developer or a team, considering factors such as maintainability, readability, and reusability. Examples include cyclomatic complexity, code duplication, and technical debt.
- Defect density: This metric measures the number of defects or bugs found in the software per thousand lines of code. A lower defect density indicates higher code quality and developer productivity.
- Lead time: This metric measures the time it takes for a software feature or bug fix to go from initial request to completion. Shorter lead times suggest higher productivity and efficiency.
- Agile metrics: Agile methodologies use various metrics to assess the productivity of software developers, including velocity (the number of user story points completed in a sprint), sprint burndown (the rate at which tasks are completed during a sprint), and team capacity (the amount of work a team can handle in a sprint).
- Code review metrics: These metrics evaluate the effectiveness of code reviews, such as the number of issues found, the time taken for reviews, and the response time for addressing feedback.
- Customer satisfaction: This metric considers the end users' satisfaction with the software, which can be measured through surveys, user feedback, and product usage data.
- It's important to note that no single productivity measure can provide a comprehensive assessment of a software developer's performance. Organizations should use a combination of metrics to gain a holistic understanding of their developers' productivity and the overall software development process.
2019
- (Hill et al., 2019) ⇒ Emerson Murphy-Hill, Ciera Jaspan, Caitlin Sadowski, David Shepherd, Michael Phillips, Collin Winter, Andrea Knight, Edward Smith, and Matthew Jorde. (2019). “What Predicts Software Developers’ Productivity?. ”IEEE Transactions on Software Engineering, 47( DOI:10.1109/TSE.2019.2900308.
- ABSTRACT: Organizations have a variety of options to help their software developers become their most productive selves, from modifying office layouts, to investing in better tools, to cleaning up the source code. But which options will have the biggest impact? Drawing from the literature in software engineering and industrial/organizational psychology to identify factors that correlate with productivity, we designed a survey that asked 622 developers across 3 companies about these productivity factors and about self-rated productivity. Our results suggest that the factors that most strongly correlate with self-rated productivity were non-technical factors, such as job enthusiasm, peer support for new ideas, and receiving useful feedback about job performance. Compared to other knowledge workers, our results also suggest that software developers' self-rated productivity is more strongly related to task variety and ability to work remotely.