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Full description
It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. In order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible, but these analysis methods can only be applied after a mature release of the code has been developed. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. We present a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software). Subjects
Computer Software |
Computer Software and Services |
Information and Communication Services |
Information and Computing Sciences |
Metrics |
Open Software |
Open source software |
PhD thesis |
Software Engineering |
Software engineering |
Software evolution |
Software maintenance |
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Identifiers
- Handle : 1959.3/190177
