Picture of DNA strand

Pioneering chemical biology & medicinal chemistry through Open Access research...

Strathprints makes available scholarly Open Access content by researchers in the Department of Pure & Applied Chemistry, based within the Faculty of Science.

Research here spans a wide range of topics from analytical chemistry to materials science, and from biological chemistry to theoretical chemistry. The specific work in chemical biology and medicinal chemistry, as an example, encompasses pioneering techniques in synthesis, bioinformatics, nucleic acid chemistry, amino acid chemistry, heterocyclic chemistry, biophysical chemistry and NMR spectroscopy.

Explore the Open Access research of the Department of Pure & Applied Chemistry. Or explore all of Strathclyde's Open Access research...

Automatically classifying test results by semi-supervised learning

Almaghairbe, Rafig and Roper, Marc (2016) Automatically classifying test results by semi-supervised learning. In: 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE). IEEE, [Piscataway, NJ], pp. 116-126. ISBN 978-1-4673-9003-3

Text (Almaghairbe-Roper-ISSRE2016-Automatically-classifying-test-results-by-semi-supervised-learning)
Accepted Author Manuscript

Download (189kB) | Preview


A key component of software testing is deciding whether a test case has passed or failed: an expensive and error-prone manual activity. We present an approach to automatically classify passing and failing executions using semi-supervised learning on dynamic execution data (test inputs/outputs and execution traces). A small proportion of the test data is labelled as passing or failing and used in conjunction with the unlabelled data to build a classifier which labels the remaining outputs (classify them as passing or failing tests). A range of learning algorithms are investigated using several faulty versions of three systems along with varying types of data (inputs/outputs alone, or in combination with execution traces) and different labelling strategies (both failing and passing tests, and passing tests alone). The results show that in many cases labelling just a small proportion of the test cases – as low as 10% – is sufficient to build a classifier that is able to correctly categorise the large majority of the remaining test cases. This has important practical potential: when checking the test results from a system a developer need only examine a small proportion of these and use this information to train a learning algorithm to automatically classify the remainder.