Abstract
Software defects are an indicator of software quality. Software with lesser number of defective modules are desired. Prediction of software defects using software measurements facilitates early identification of defect-prone modules. Association relationship between software measures and defects improves prediction of defective modules. To find association relationship between software measures and defects, each numeric measure is divided into bins. Each bin is called 1-itemset (or an itemset of length 1). When certain itemsets and defective modules appear together in a dataset, they are considered associated with each other. Frequency of their co-occurrence depicts the strength of the association relationship. Existing studies find the relationship between 1-itemsets and defective modules. Itemsets that have high association with defects are called focused itemsets. Focused itemsets can be used to build prediction models with higher Recall values. This paper explores the relationship between defective modules and itemsets with length greater than 1. Focused itemsets with length greater than 1 involve multiple bins at same time. Identification of the focused itemsets has improved the performance of decision tree based defect prediction model.
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Rana, Z.A., Malik, S.A., Shamail, S., Awais, M.M. (2013). Identifying Association between Longer Itemsets and Software Defects. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_18
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DOI: https://doi.org/10.1007/978-3-642-42051-1_18
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