Detecting Defects with an Interactive Code Review Tool Based on Visualisation and Machine Learning

Document type: Conference Papers
Peer reviewed: Yes
Full text:
Author(s): Stefan Axelsson, Dejan Baca, Robert Feldt, Darius Sidlauskas, Denis Kacan
Title: Detecting Defects with an Interactive Code Review Tool Based on Visualisation and Machine Learning
Translated title: Detektion av defekter med ett interkativt kodreviewverktyg baserat på visualisering och maskininlärning
Conference name: The 21st International Conference on Software Engineering and Knowledge Engineering (SEKE 2009)
Year: 2009
City: Boston, USA
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/com
Authors e-mail: stefan.axelsson@bth.se, robert.feldt@bth.se, dejan.baca@bth.se
Language: English
Abstract: Code review is often suggested as a means of improving code quality. Since humans are poor at repetitive tasks, some form of tool support is valuable. To that end we developed a prototype tool to illustrate the novel idea of applying machine learning (based on Normalised Compression Distance) to the problem of static analysis of source code. Since this tool learns by example, it is rivially programmer adaptable. As machine learning algorithms are notoriously difficult to understand operationally (they are opaque) we applied
information visualisation to the results of the learner. In order to validate the approach we applied the prototype to source code from the open-source project Samba and from an industrial, telecom software system. Our results showed that the tool did indeed correctly find and classify problematic sections of code based on training examples.
Subject: Computer Science\Artificial Intelligence
Computer Science\Electronic security
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