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EkmEx - an extended framework for labeling an unlabeled fault dataset

  • 1177: Advances in Deep Learning for Multimodal Fusion and Alignment
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Abstract

Software fault prediction (SFP) is a quality assurance process that identifies if certain modules are fault-prone (FP) or not-fault-prone (NFP). Hence, it minimizes the testing efforts incurred in terms of cost and time. Supervised machine learning techniques have capacity to spot-out the FP modules. However, such techniques require fault information from previous versions of software product. Such information, accumulated over the life-cycle of software, may neither be readily available nor reliable. Currently, clustering with experts’ opinions is a prudent choice for labeling the modules without any fault information. However, the asserted technique may not fully comprehend important aspects such as selection of experts, conflict in expert opinions, catering the diverse expertise of domain experts etc. In this paper, we propose a comprehensive framework named EkmEx that extends the conventional fault prediction approaches while providing mathematical foundation through aspects not addressed so far. The EkmEx guides in selection of experts, furnishes an objective solution for resolve of verdict-conflicts and manages the problem of diversity in expertise of domain experts. We performed expert-assisted module labeling through EkmEx and conventional clustering on seven public datasets of NASA. The empirical outcomes of research exhibit significant potential of the proposed framework in identifying FP modules across all seven datasets.

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Authors and Affiliations

  1. Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan

    Muhammad Rizwan & Aamer Nadeem

  2. Department of Computer Science, London South Bank University, London, England

    Sohail Sarwar & Muddesar Iqbal

  3. Department of Computer Science, GC University, Lahore, Pakistan

    Muhammad Safyan

  4. Department of Computer Science, Allama Iqbal Open University, Islamabad, Pakistan

    Zia Ul Qayyum

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  1. Muhammad Rizwan
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Rizwan, M., Nadeem, A., Sarwar, S. et al. EkmEx - an extended framework for labeling an unlabeled fault dataset. Multimed Tools Appl 81, 12141–12156 (2022). https://doi.org/10.1007/s11042-021-11441-7

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