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Software Evolution |
Vulnerable Components |
Predicting Failures |
Good Bug Reports |
Related Changes |
Cross-cutting Concerns |
Usage Patterns |
The Software Evolution project at the Software Engineering Chair, Saarland University, analyzes version and bug databases to predict failure-prone modules, related changes, and future development activities.
Programmers who changed this function also changed...
If you browse the books at Amazon or a similar shop, you may have
encountered suggestions of this type: ``Customers who bought this book
also bought...'' Such findings stem from Amazon's purchase history:
Buying two books or more together establish a relationship between
these two books.
We realized a similar feature for software: "Programmers who
changed function X also changed function Y". For this purpose, we
analyze version histories of large software systems, trying to
identify commonalities and anomalities, and guiding the programmer in
understanding and maintenance.
Mining Version Histories to Guide
Software Changes. T. Zimmermann, P. Weißgerber,
S. Diehl, A. Zeller.
26th International Conference on Software Engineering (ICSE), Edinburgh, UK, May 2004.
We apply data mining to version histories in order to guide
programmers along related changes: "Programmers who changed these
functions also changed...". Given a set of existing changes, such
rules a) suggest and predict likely further changes,
b) show up item coupling that is indetectable
by program analysis, and
c) prevent errors due to incomplete changes.
Our evaluation shows after an initial change, our ROSE prototype can
correctly predict 26% of further files to be changed—and 15%
of the precise functions or variables. 30% of the suggested files
and 26% of the suggested functions or variables were correct
Preprocessing CVS Data for Fine-Grained Analysis. T. Zimmermann, P. Weißgerber.
International Workshop on Mining Software Repositories (MSR), Edinburgh, UK, May 2004.
All analyses of version archives have one phase in common: the preprocessing of data. Preprocessing has a direct impact on the quality of the results returned by an analysis. In this paper we discuss four essential preprocessing tasks necessary for a fine-grained analysis of CVS archives: data extraction, transaction recovery, mapping of changes to fine-grained entities, and data cleaning. We formalize the concept of sliding time windows and show how commit mails can relate revisions to transactions. We also present two approaches that map changes to the affected building blocks of a file, e.g. functions or sections.
How History Justifies System
Architecture (or not). T. Zimmermann,
S. Diehl, A. Zeller.
International Workshop on Principles of Software Evolution
(IWPSE 2003), Helsinki, Finland, September 2003.
Abstract. The revision history of a software
system conveys important information about how and why the system
evolved in time. The revision history can also tell us which parts
of the system are coupled by common changes: "Whenever the database
schema was changed, the sqlquery() method was altered,
too." This "evolutionary" coupling can be compared with the coupling
as imposed by the system architecture; differences indicate
anomalies which may be subject to restructuring.
Our ROSE prototype analyzes fine-grained coupling between software
entities as indicated by common changes. It turns out that common
changes are a good indicator for modularity, that evolutionary
coupling should be determined between syntactical entities (rather
than files or modules), and that common changes can indicate
coupling between software entities and non-program artifacts that is
unavailable to the analysis of a single version.
- eROSE: Guiding Programmers in Eclipse
Keep me posted
<firstname.lastname@example.org> · http://www.st.cs.uni-saarland.de/softevo/changes.php · Stand: 2018-04-05 13:41