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Topic modeling and intuitionistic fuzzy set-based approach for efficient software bug triaging

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Abstract

Modern software development involves multiple developers working remotely in a distributed manner around the world. Software bugs are continuously generated for multiple reasons across various modules. It is possible that one software bug can affect multiple modules, and there can be multiple developers associated with it. Furthermore, many software bug reports are unlabeled, vague, and noisy. The triager faces significant challenges in identifying multiple causes of software bugs and finding expert developers for bug fixing. In this paper, the fuzzy set is extended to Intuitionistic Fuzzy Sets (IFS), and a novel bug triaging approach based on Intuitionistic Fuzzy Similarity (IFSim) measures is presented to overcome the aforementioned problems. The topic model is used to discover multiple relationships between developers and software bugs. IFS is used to separate developers based on their degree of membership and non-membership in a particular software category, with a degree of hesitation for some developers. For a new bug, 15 different IFSim measure techniques are investigated to compute the similarity with the existing software bugs. Finally, a fuzzy \(\alpha \)-cut is applied to find expert developers to repair it. The best results are obtained by considering the number of topics of 15 and 12 taxonomic terms for each topic. Among all the IFSim measure techniques, the similarity techniques proposed by Ye outperform other techniques. Experiments are carried out on available benchmark data sets, and the results are compared to traditional machine learning algorithms and the fuzzy logic-based Bugzie model.

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Notes

  1. https://bugs.eclipse.org/bugs/.

  2. https://bugs.eclipse.org/bugs/.

  3. https://bugs.eclipse.org/bugs/.

  4. https://bugzilla.mozilla.org/describecomponents.cgi.

  5. https://netbeans.org/bugzilla/.

  6. https://www.r-project.org.

  7. https://bugs.eclipse.org/bugs/.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Raipur, India

    Rama Ranjan Panda & Naresh Kumar Nagwani

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  1. Rama Ranjan Panda
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Correspondence to Rama Ranjan Panda or Naresh Kumar Nagwani.

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Appendices

Illustrative example

A sample of the Eclipse data setFootnote 7 is used to illustrate the proposed approach. Initially, a fixed number of \(S_\mathrm{b}\) in a given range is considered for conducting the experiment. Here, all the developers having bug counts between 45 and 70 are selected, and it is illustrated in Table 8.

Table 8 A sample bug distribution between developers for Eclipse data set
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In this article, the actual names of developers involved with the bug data set are not listed for privacy purposes. The sample data is divided into training and testing data. The training data consists of 251 software bugs, and the testing data consists of 59 software bugs. Initially, the pre-processing is performed for training data and the pre-processed software bugs are transformed into \(D_\mathrm{t}\) matrix for finding the occurrence of terms associated with each developer. The \(D_\mathrm{t}\) matrix has 5 observations with 709 terms.

A sample of the developer-associated term matrix is shown in Table 9. In the next step, the number of topics and their taxonomic terms are selected based on the user. LDA is used to create 10 topics and for each topic 5 taxonomic terms are considered for further operation. The list of taxonomic terms and the associated topics is shown in Table 10. Then the taxonomic terms of each topic are mapped with \(D_\mathrm{t}\) matrix to compute the total number of the taxonomic terms of each developer. For Topic1, the computed values are shown in Table 11. The total column in Table 11 shows the total number of taxonomic terms for each developer present in a particular topic. In the same way, for other topics, the total number of taxonomic terms is calculated, and it is shown in Table 12. The data in Table 12 shows the total number of terms that each developer belongs to each topic.

Once the relationship between the developer and taxonomic terms is determined for training data, the same thing will be carried out for testing data without considering the name of the developer of each software bug. Among the 60 testing data a sample of 2 testing data is shown in Table 13. In the next step, the \(\mu _F(x)\), \(\nu _F(x)\) and \(\pi _F(x)\) values are computed for training and testing data. The computed IFS membership grade, non-membership grade and hesitancy grade for training data set is shown in the Tables 14, 15 and 16 respectively. The IFS for a developer \(D_A\) with Topic 1 in the training data set is represented as \(F=\{(x,0.080,0.885,0.035)\}\). Similarly, the computed IFS membership grade, non-membership grade, and hesitancy grade for the testing data set is shown in the Tables 17, 18 and 19 respectively. The IFS for test-1 in testing data with Topic 2 is represented as \(F=\{(x,0.333,0.572,0.095)\}\).Once the training and testing data are represented using IFS, the different existing IFSim measures listed in Table 3 are used to find the similarity between the training and testing data. Here in this illustrative example, \(S_L(P,Q)\) is utilized to compute the similarity between testing data (Test 1 and Test 2) with training data and it is shown in the Table 20. To identify experts for fixing Test 1 and Test 2, a fuzzy \(\alpha \)-cut is applied to the computed similarity values. Suppose the \(\alpha \) value is 0.7, then for fixing Test 1, the expert developers are \(D_A\), \(D_C\) and \(D_E\), and for Test 2, all developers are eligible.

Table 9 Developer-associated term matrix
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Table 10 Topic and its taxonomic terms
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Table 11 Taxonomic terms and its count for Topic 1
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Table 12 Developer and taxonomic terms count for each topic
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Table 13 A sample of test data set with its taxonomic terms count for each topic
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Table 14 IFS membership grade of each developer associated with each topic for training data set
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Table 15 IFS non-membership grade of each developer associated with each topic for training data set
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Table 16 IFS hesitant grade of each developer associated with each topic for training data set
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Table 17 IFS membership grade of each developer associated with each topic for testing data set
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Table 18 IFS non-membership grade of each developer associated with each topic for testing data set
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Table 19 IFS hesitant grade of each developer associated with each topic for testing data set
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Table 20 Similarity values of test 1 and test 2 with training data
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Experimental results

See Tables 21, 22, 23, and 24.

Table 21 Effect of number of topics and taxonomic terms on different data sets
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Table 22 Performance measure of IFS using different similarity techniques on Eclipse data set
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Table 23 Performance measure of IFS using different similarity techniques on Mozilla data set
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Table 24 Performance measure of IFS using different similarity techniques on NetBeans data set
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Panda, R.R., Nagwani, N.K. Topic modeling and intuitionistic fuzzy set-based approach for efficient software bug triaging. Knowl Inf Syst 64, 3081–3111 (2022). https://doi.org/10.1007/s10115-022-01735-z

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