Proposing an evolutionary-fuzzy method for software defects detection

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EFTEKHARI MAHDI; Majidi Momenabadi Maryam; Khamar Mojtaba

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Journal: SIGNAL AND DATA PROCESSING Year:2019 | Volume:15 | Issue:4 (38) Page(s): 3-16

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Title

Proposing an evolutionary-fuzzy method for software defects detection

Author(s)

EFTEKHARI MAHDI | Majidi Momenabadi Maryam | Khamar Mojtaba | Issue Writer Certificate

Keywords

classificationQ1

evolutionary algorithmQ1

fuzzy logicQ2

imbalance datasetsQ1

software defect detectionQ2

Abstract

Software defects detection is one of the most important challenges of software development and it is the most prohibitive process in software development. The early detection of fault-prone modules helps software project managers to allocate the limited cost, time, and effort of developers for testing the defect-prone modules more intensively. In this paper, according to the importance of software defects detection, a method based on fuzzy sets and evolutionary algorithms is proposed. Due to the imbalanced nature of software defect detection datasets, benefits of fuzzy clustering algorithms were used to data sampling and more attention to the minority class. This method is a combined algorithm which, firstly has used fuzzy c-mean clustering as weighted bootstrap sampling. Weight of data (their membership’ s degrees) increases for minority class. In the next step, the subtractive clustering algorithm is applied to produce the classifier which was trained by produced data in the previous step. The binary genetic algorithm was utilized to select appropriate features. The results and also comparisons with eight popular methods in software defect detection literature, show an acceptable performance of the proposed method. The experiments were performed on ten real-world datasets with a wide range of data sizes and imbalance rates. Also T-test is used as the statistical significance test for pair wise comparison of our proposed method against the others. The final results of T-test are shown in tables for three performance measures (G-mean, AUC and Balanced) over various datasets. (As the obtained results apparently show our proposed method has the ability to improve three aforementioned performance criteria simultaneously). Some methods just have improved the G-mean measure while the AUC and Balance criteria have lower values than the others. Securing a high level of three performance measures simultaneously illustrates the ability of our proposed algorithm for handling the imbalance problem of software defects detection datasets.

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APA: Copy

EFTEKHARI, MAHDI, Majidi Momenabadi, Maryam, & Khamar, Mojtaba. (2019). Proposing an evolutionary-fuzzy method for software defects detection. SIGNAL AND DATA PROCESSING, 15(4 (38) ), 3-16. SID. https://sid.ir/paper/160851/en

Vancouver: Copy

EFTEKHARI MAHDI, Majidi Momenabadi Maryam, Khamar Mojtaba. Proposing an evolutionary-fuzzy method for software defects detection. SIGNAL AND DATA PROCESSING[Internet]. 2019;15(4 (38) ):3-16. Available from: https://sid.ir/paper/160851/en

IEEE: Copy

MAHDI EFTEKHARI, Maryam Majidi Momenabadi, and Mojtaba Khamar, “Proposing an evolutionary-fuzzy method for software defects detection,” SIGNAL AND DATA PROCESSING, vol. 15, no. 4 (38) , pp. 3–16, 2019, [Online]. Available: https://sid.ir/paper/160851/en

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