Mining Source Code for Structural Regularities

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Mining Source Code for Structural Regularities

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dc.contributor.author Lozano, Ángela
dc.contributor.author Kellens, Andy
dc.contributor.author Mens, Kim
dc.contributor.author Arevalo, Gabriela
dc.date.accessioned 2017-04-11T12:40:42Z
dc.date.available 2017-04-11T12:40:42Z
dc.date.issued 2017-04-11
dc.identifier.uri http://hdl.handle.net/123456789/429
dc.description.abstract During software development, design rules and contracts in the source code are often encoded through regularities, such as API usage protocols, coding idioms and naming conventions. The structural regularities that govern a program can aid in comprehension and maintenance of the application, but are often implicit or undocumented. Tool support for extracting these regularities from the source code can provide developers useful insights. But building such tool support is not trivial, in particular, because the informal nature of regularities results in frequent deviations and exceptions to these regularities. We propose an automated approach, based on association rule mining, to discover the structural regularities that govern the source code of a software system.We chose this technique because of its resilience to exceptions. In general, tool support for mining regularities tends to discover a huge amount of rules, making interpretation of the results hard and time-consuming. To ease the interpretation, we reduce the results to a minimal canonical form, and group them to obtain a more rational description of the discovered regularities. As an initial feasibility study of our approach, we applied it on two open-source systems, namely IntensiVE (Smalltalk) and FreeCol (Java). en_US
dc.language.iso en en_US
dc.title Mining Source Code for Structural Regularities en_US
dc.type Preprint en_US


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