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A Novel Integrated Framework to Increase Software Quality by Mining Source Code

Source code contain lot of structural features that embody latent information
that if identified can help software engineers to develop quality software in
least amount of time. For instance, many programming rules are hidden in set
of function calls, variable usage, data accesses in functions, object interaction
etc. that seldom exist outside the minds of developers. Violations of these
rules may introduce bugs which are difficult to uncover, report to bug-tracking
systems and fix unless the rules are explicitly documented and made available
to the development team. In order to address this problem there is a need to
apply strong analysis techniques on source code to find latent programming patterns
that can be potentially useful for performing various software engineering tasks.
This study demonstrates how data mining techniques can be applied on source
code to improve software quality and productivity by proposing a framework.
This new approach is able to find different programming patterns such as programming
rules, variable correlation, code clones and frequent API usage patterns. Furthermore,
efficient algorithms are proposed to automatically detect violation to the extracted
rules. Proposed framework is validated by developing a prototype and evaluated
on various projects of significant size and complexity. Results shows proposed
technique greatly reduced time and cost of manually checking defects from source
code by programmers.

Journal of Software Engineering 2013/06/21 - 13:57 Czytaj