Machine Learning Based Software Defect Categorization Using Crowd Labeling

Sushil Kumar (), Meera Sharma (), S. K. Muttoo () and V. B. Singh ()
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Sushil Kumar: Shyam Lal College, University of Delhi
Meera Sharma: Swami Shraddhanand College, University of Delhi
S. K. Muttoo: University of Delhi
V. B. Singh: Jawaharlal Nehru University

A chapter in Predictive Analytics in System Reliability, 2023, pp 213-227 from Springer

Abstract: Abstract Defect categorization is an important task which helps in software maintenance. It also helps in prioritizing the defects, resource allocation, etc. Standard machine learning techniques can be used to automate the categorization of defects. Labeled data is needed for learning models. The expert is required for obtaining the labeled data. Sometimes, it is costly or expert is not available. So, to overcome this dependency, crowd labeled data is used to train a model. Crowd (a set of novices) is asked to assign a category as defined by IBM’s Orthogonal Defect Classification (ODC) to the defect reports. Obtaining categories through crowd can be inaccurate or noisy. Inferencing ground truth is a challenge in crowd labeling. Support Vector Machine, k Nearest Neighbor and Gaussian Naive Bayes classifier, are learnt effectively using new methodology from data labeled by a set of novices. In this chapter, we have proposed a learning model which learns effectively to predict the impact category of software defects using the expectation maximization algorithm and shows the better performance according to the various types of metrics by improving the existing technique by 8% and 11% accuracy for Compendium and Mozilla datasets respectively.

Keywords: Crowd labeling; Naive Bayes classifier; Categorization; Expectation maximization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-05347-4_14

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