A fault prediction model with limited fault data to improve test process

  • Catal C
  • Diri B
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Abstract

Software fault prediction models are used to identify the fault-prone software modules and produce reliable software. Performance
of a software fault prediction model is correlated with available software metrics and fault data. In some occasions, there
may be few software modules having fault data and therefore, prediction models using only labeled data can not provide accurate
results. Semi-supervised learning approaches which benefit from unlabeled and labeled data may be applied in this case. In
this paper, we propose an artificial immune system based semi-supervised learning approach. Proposed approach uses a recent
semi-supervised algorithm called YATSI (Yet Another Two Stage Idea) and in the first stage of YATSI, AIRS (Artificial Immune
Recognition Systems) is applied. In addition, AIRS, RF (Random Forests) classifier, AIRS based YATSI, and RF based YATSI are
benchmarked. Experimental results showed that while YATSI algorithm improved the performance of AIRS, it diminished the performance
of RF for unbalanced datasets. Furthermore, performance of AIRS based YATSI is comparable with RF which is the best machine
learning classifier according to some researches.

Author-supplied keywords

  • AIRS
  • Artificial immune systems
  • Semi-supervised learning
  • Software fault prediction
  • YATSI

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