Predicting Source Code Quality with Static Analysis and Machine Learning

This paper is investigating if it is possible to predict source code qualitybased on static analysis and machine learning. The proposed approachincludes a plugin in Eclipse, uses a combination of peer review/humanrating, static code analysis, and classification methods. As training data,public data and student hand-ins in programming are used. Based onthis training data, new and uninspected source code can be accuratelyclassified as “well written” or “badly written”. This is a step towardsfeedback in an interactive environment without peer assessment

Predicting regression test failures using genetic algorithm-selected dynamic performance analysis metrics

A novel framework for predicting regression test failures is proposed. The basic principle embodied in the framework is to use performance analysis tools to capture the runtime behaviour of a program as it executes each test in a regression suite. The performance information is then used to build a dynamically predictive model of test outcomes. Our framework is evaluated using a genetic algorithm for dynamic metric selection in combination with state-of-the-art machine learning classifiers. We show that if a program is modified and some tests subsequently fail, then it is possible to predict with considerable accuracy which of the remaining tests will also fail which can be used to help prioritise tests in time constrained testing environments