Software archives mining deals with the automated extraction, collection, and abstraction of data from the information generated during the software development process (e.g. source code archives, bug tracking systems, etc.). This seminar (7 CP) introduces the notion of software archives and teaches recent software archives mining techniques. More details here

Developers change source code to add new functionality, fix bugs, or refactor their code. Many of these changes have immediate impact on quality or stability. However, some impact of changes may become evident only in the long term. The goal of this thesis is to explore the long-term impact of changes by detecting dependencies between code changes and by measuring their influence on software quality, software maintainability, and development effort. Being able to identify the changes with the greatest long-term impact will strengthen our understanding of a project’s history and thus shape future code changes and decisions.

Reference

BibTeX Entry

@inproceedings{herzig-icse-2010,
 author = "Herzig, Kim Sebastian",
 title = "Capturing the long-term impact of changes",
 booktitle = "ICSE '10: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering",
 year = "2010",
 isbn = "978-1-60558-719-6",
 pages = "393--396",
 location = "Cape Town, South Africa",
 doi = "http://doi.acm.org/10.1145/1810295.1810401",
 publisher = "ACM",
 address = "New York, NY
}

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One major down side of many defect prediction models is the frequent usability. As an example: A prediction model based on code metrics only, predicts the number of defects based upon the code complexity of each entity. Now, if the programmer fixes the bug, he might change only one line and therefore does not change the code complexity significantly. The complexity might even increase. Using the same defect prediction model again gives us the same result, because the basis of the defect prediction model did not changed or did not changed significantly.

To overcome such problems, we could use a dynamic approach. But most dynamic approaches cannot predict any defects before defects have been discovered.

The idea is to use different time related properties of a code entity to determine the age of this entity. The main assumption is that older entities have fewer defects. Defining the term older entities is the main task of this project.