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Adaptive Ensemble Learning Model-Based Binary White Shark Optimizer for software defect classification

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-51页

作者： Saraireh, Jameel Agoyi, Mary Kassaymeh, Sofian Cyprus Int Univ Dept Management Informat Syst Nicosia North Cyprus Turkiye Cyprus Int Univ Dept Informat Technol Haspolat Lefkossa Turkiye Jadara Univ Jadara Univ Res Ctr Irbid Jordan Aqaba Univ Technol Software Engn Dept Aqaba Jordan

software dominates modern enterprises, affecting numerous functions. software firms constantly experiment with new methodologies to define and assess software quality to stay competitive and ensure excellence. software engineering uses fundamentals and cutting-edge technology to develop great software. In recent decades, Data-mining techniques and machine learning for classifying problematic software projects have emerged to improve software quality. ML approaches, especially ensemble learning models, are becoming fundamental to software engineers' daily jobs. This work created a binary white shark optimizer (WSO) to optimize standard ensemble learning models. The objective is to identify the most suitable ensemble number for weak learners to maximize accuracy on benchmark datasets. The EM model uses 14 weak learners. Twenty-one experimental runs are performed on 15 software-defective module datasets. The optimized ensemble model outperforms the standard Ensemble learning model in AUC-ROC, Accuracy, Precision, Recall, F1-Score, and Specificity. The enhanced model has an average accuracy of 86%, compared to 76% for the standard ensemble model across all datasets. The optimized model outperformed the conventional ensemble for the same datasets, with an average AUC of 72% compared to 61% for the standard ensemble. The optimized model was more stable than the standard model, with an STD of 5.53E-03 vs 7.24E-02 for the ensemble model. The WSO optimization process strengthens and generalizes optimizeels. The study suggests that evolutionary metaheuristic approaches can enhance EM models' accuracy, trustworthiness, and adaptability.

关键词： Binary white shark optimizer Ensemble learning software defect classification software quality

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Predicting software defect type using concept-based classification

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EMPIRICAL software ENGINEERING 2020年第2期25卷 1341-1378页

作者： Patil, Sangameshwar Ravindran, B. IIT Madras Dept Comp Sci & Engn Chennai Tamil Nadu India TCS Res Pune Maharashtra India IIT Madras Robert Bosch Ctr Data Sci & AI Chennai Tamil Nadu India

Automatically predicting the defect type of a software defect from its description can significantly speed up and improve the software defect management process. A major challenge for the supervised learning based current approaches for this task is the need for labeled training data. Creating such data is an expensive and effort-intensive task requiring domain-specific expertise. In this paper, we propose to circumvent this problem by carrying out concept-based classification (CBC) of software defect reports with help of the Explicit Semantic Analysis (ESA) framework. We first create the concept-based representations of a software defect report and the defect types in the software defect classification scheme by projecting their textual descriptions into a concept-space spanned by the Wikipedia articles. Then, we compute the "semantic" similarity between these concept-based representations and assign the software defect type that has the highest similarity with the defect report. The proposed approach achieves accuracy comparable to the state-of-the-art semi-supervised and active learning approach for this task without requiring labeled training data. Additional advantages of the CBC approach are: (i) unlike the state-of-the-art, it does not need the source code used to fix a software defect, and (ii) it does not suffer from the class-imbalance problem faced by the supervised learning paradigm.

关键词： software defect classification software defect management Natural language processing Explicit semantic analysis Orthogonal defect classification

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software engineering defect detection and classification system based on artificial intelligence

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NONLINEAR ENGINEERING - MODELING AND APPLICATION 2022年第1期11卷 380-386页

作者： Wang, Hong Yuan, Limin Shijiazhuang Informat Engn Vocat Coll Dept Media Arts Shijiazhuang 050035 Hebei Peoples R China Hebei Chem & Pharmaceut Coll Sci Res Dept Shijiazhuang 050026 Hebei Peoples R China

With the increasing reliance on automatic software-based applications, it is important to automate the classification of software defects and ensure software reliability. An automatic software defect classification system based on an expert system is proposed in this article. In this method, DACS first determines the category of software defects through the selection of typical features, then reduces the spatial knowledge base searched by the inference engine and selects the characteristics of a certain type of defect. Make a selection, determine the name of the defect, and finally select different causes and prevention methods for the defect as needed. The DACS structure was built, and the experiment showed that the AI system took 15 s to complete, whereas the traditional mechanism took 48 s;the accuracy of the AI was 99%, whereas the accuracy of the traditional mechanism was only 68%. According to the aforementioned experimental results, the recognition accuracy of the proposed research scheme is higher than that of the traditional mechanism. Hence, the time required to solve the problem of software engineering defect detection and classification is less than that of the traditional mechanism.

关键词： expert system software defect detection software defect classification

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software Complexity Metric-based defect classification Using FARM with Preprocessing Step CFS and SMOTE A Preliminary Study 2

Software Complexity Metric-based Defect Classification Using...

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International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Naufal, Mohammad Farid Rochimah, Siti Inst Teknol Sepuluh Nopember Dept Informat Surabaya Indonesia

ISBN: (纸本)9781467366649

One criteria for assessing the software quality is ensuring that there is no defect in the software which is being developed. software defect classification can be used to prevent software defects. More earlier software defects are detected in the software life cycle, it will minimize the software development costs. This study proposes a software defect classification using Fuzzy Association Rule Mining (FARM) based on complexity metrics. However, not all complexity metrics affect on software defect, therefore it requires metrics selection process using Correlation-based Feature Selection (CFS) so it can increase the classification performance. This study will conduct experiments on the NASA MDP open source dataset that is publicly accessible on the PROMISE repository. This datasets contain history log of software defects based on software complexity metric. In NASA MDP dataset the data distribution between defective and not defective modules are not balanced. It is called class imbalanced problem. Class imbalance problem can affect on classification performance. It needs a technique to solve this problem using oversampling method. Synthetic Minority Oversampling Technique (SMOTE) is used in this study as oversampling method. With the advantages possessed by FARM in learning on dataset which has quantitative data attribute and combined with the software complexity metrics selection process using CFS and oversampling using SMOTE, this method is expected has a better performance than the previous methods.

关键词： Bugs defect Fault Fuzzy Association Rule Mining Machine Learning software defect classification Correlation-based Feature Selection Synthetic Minority Oversampling Technique

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Concept-based classification of software defect Reports 14

Concept-based Classification of Software Defect Reports

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IEEE/ACM 14th International Conference on Mining software Repositories (MSR)

作者： Patil, Sangameshwar Tata Consultancy Serv TRDDC Pune Maharashtra India ITT Madras Dept Comp Sci & Engg Madras Tamil Nadu India

ISBN: (纸本)9781538615447

Automatic identification of the defect type from the textual description of a software defect can significantly speed-up as well as improve the software defect management life cycle. This has been recognized in the research community and multiple solutions based on supervised learning approach have been proposed in the recent literature. However, these approaches need significant amount of labeled training data for use in real life projects. In this paper, we propose to use Explicit Semantic Analysis (ESA) to carry out concept-based classification of software defect reports. We compute the "semantic similarity" between the defect type labels and the defect report in a concept space spanned by Wikipedia articles and then, assign the defect type which has the highest similarity with the defect report. This approach helps us to circumvent the problem of dependence on labeled training data. Experimental results show that using concept based classification is a promising approach for software defect classification to avoid the expensive process of creating labeled training data and yet get accuracy comparable to the traditional supervised learning approaches. To the best of our knowledge, this is the first use of Wikipedia and ESA for software defect classification problem.

关键词： software defect classification Explicit Semantic Analysis Mining software Respositories Text Data Mining

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Three-way decisions based software defect prediction

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KNOWLEDGE-BASED SYSTEMS 2016年 91卷 263-274页

作者： Li, Weiwei Huang, Zhiqiu Li, Qing Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China PLA Univ Sci & Technol Coll Command Informat Syst Nanjing 210007 Jiangsu Peoples R China

Based on a two-stage classification method and a two-stage ranking method on three-way decisions, this paper introduces a three-way decisions framework for cost-sensitive software defect prediction. For the classification problem in software defect prediction, traditional two-way decisions methods usually generate a higher classification error and more decision cost. Here, a two-stage classification method that integrates three-way decisions and ensemble learning to predict software defect is proposed. Experimental results on NASA data sets show that our method can obtain a higher accuracy and a lower decision cost. For the ranking problem in software defect prediction, a two-stage ranking method is introduced. In the first stage, all software modules are classified into three different regions based on three-way decisions. A dominance relation rough set based ranking algorithm is next applied to rank the modules in each region. Comparison experiments with 6 other ranking methods present that our proposed method can obtain a better result on FPA measure. (C) 2015 Elsevier B.V. All rights reserved.

关键词： software defect classification software defect ranking Three-way decisions

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Severe software defects Trigger Factors: A Case Study of a School Management System

Severe Software Defects Trigger Factors: A Case Study of a S...

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International Conference on Digital Science (DSIC)

作者： Hillah, Nico Univ Lausanne Lausanne Switzerland

ISBN: (纸本)9783030023515;9783030023508

In this paper, we identify the groups of triggers that are responsible for severe software failures. These failures prevent any essential operation or activity to be conducted through the concerned system or other systems connected to it. In fact, the occurrence of these failures causes a double financial cost to organizations: one in fixing them and the other one because of the unavailability of the system or systems. We targeted three types of software defects as sources of these failures. We conducted this study by classifying 665 software defects of a school management system and we found that the top two trigger groups are the technology and the IS architecture groups.

关键词： software defect severity software defect triggers software defect classification

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Data-efficient software defect prediction: A comparative analysis of active learning-enhanced models and voting ensembles

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INFORMATION SCIENCES 2024年 676卷

作者： Liapis, Charalampos M. Karanikola, Aikaterini Kotsiantis, Sotiris Univ Patras Dept Math Patras 26504 Greece Comp Technol Inst & Press Diophantus Patras 26504 Greece

As software systems undergo escalating complexity, the identification of bugs and defects becomes pivotal for ensuring seamless user experiences and averting potentially costly post -release issues. This study addresses this critical need by concentrating on the application of active learning methods in code defect prediction. The investigation focuses on the efficacy of active learning combined with ensemble methods, leveraging the dynamic selection and labeling of training instances to increase model performance, reduce the demand for exhaustive labeling efforts, and enhance the effectiveness of code defect prediction systems. Various traditional and ensemble methods are deployed, employing diverse query strategies (uncertainty, margin, and entropy sampling) to assess if active variants can rival original approaches while significantly downsizing the training set. Evaluation encompasses classical classification metrics (AUC, Kappa, and MCC), supplemented by a proposed easy -to -interpret performance index that takes into consideration not only the traditional metric outcomes but also the percentage of the initial dataset utilized, aligned with the dual nature of the problem. The results, elucidated through graphical representations and statistical tests, unveil the advantage of active methods, showcasing reductions of the initial training set by at least 75% in approximately 64% of cases.

关键词： Active learning software defect classification Bug classification Ensemble learning Voting classification Interpretable classification

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