Software Fault Research Articles

Convolutional Neural Networks for Software Defect Categorization: An Empirical Validation

The escalating complexity and scale of software systems have rendered them increasingly susceptible to a variety of defects. To empower maintenance teams to efficiently prioritize and resolve defects, Software Defect Categorization (SDC) models have emerged, offering the classification of software defects into categories such as "high," "medium," or "low." This study embarks on the development of SDC models, based on three critical defect attributes: i) the maintenance effort required to rectify a defect, ii) the change impact on the software induced by defect resolution, and iii) a combined approach that integrates both maintenance effort and change impact. Leveraging the prevailing advancements in computational power and storage capacity, the study present a novel defect categorization model built upon Convolutional Neural Networks (CNNs). Extensive experiments were carried out on defect datasets from five Android operating system application modules, leading to the creation of 60 SDC models (5 datasets x 4 feature sets x 3 approaches). The results underscore the predictive potential of our CNN-based defect categorization model. Furthermore, SDC models rooted in the combined approach exhibit superior performance when compared to models based solely on change impact and remain competitive with those anchored in maintenance effort.

Editorial

Dear Readers,  I would like to wish you all the best for the new year! It is with great pleasure that I welcome you to our first regular issue in 2025 with a pleasant new achievement. The journal has successfully exceeded 30 years and is still available to authors and readers without interruption. I would like to gratefully acknowledge the visionary ideas of Prof. Hermann Maurer, who founded the journal and successfully managed it for many years, preparing the ground for it to become one of the longest-running open content journals in computer science. Starting with this issue in the new year, we are very pleased to be part of the KOALA Computer Science Cluster from TIB - Leibniz Information Centre for Science and Technology University Library, Germany. We have also changed our license to CC BY, and future special issues will also be free of charge because of the KOALA funding.  Looking back on the past year, we have further increased our visibility and taken steps to fully comply with the Diamond Open Content Standard and align our journal with the KOALA requirements. Thanks to the combined efforts of the Pensoft team and the J.UCS publishing team, we are listed and indexed in more than 40 indexing services worldwide, including DOAJ, Web of Science, and Scopus. The increased visibility and social media presence have also led to a further increase in page views and article downloads. With around 120,000 unique views, reader interest in J.UCS publications increased by 20% compared to the previous year. We can also look back on an increasing number of submitted articles and special issue proposals. This interest is reflected in a notable impact factor with a Scopus Cite Score of 2.2 and a Web of Science Journal Impact Factor of 0.7. We proudly look back on a total of 13 issues - 12 regular and 1 special issue - with 78 articles by 256 authors from 45 countries on novel aspects of various computer science topics. The acceptance rate has fallen to below 15 per cent.  These great achievements were only possible thanks to the commitment and interest of the community and the valuable support of the Editorial Board and financial supporters of J.UCS. In 2024, we welcomed 19 new members to the Editorial Board, bringing our total number of Editorial Board members to 211. We would also like to gratefully acknowledge the support of 33 guest reviewers over the past year.   In particular, I would like to thank Dr. Ulrike Krießmann from the Library of the Graz University of Technology, the TIB - Leibniz Information Centre for Science and Technology University Library in Germany, and the Institute of Interactive Systems and Data Science from TU Graz for their financial support.   I would also like to thank the J.UCS team, Johanna Zeisberg for taking care of the publication process, David Kerschbaumer for his social media support, and Sebastian Gürtl and Alexander Nussbaumer for their technical support, as well as Pensoft Publishers Ltd. for hosting our journal.  I look forward to continuing to work with our editors, editorial team and technical support to maintain the success of J.UCS. I would be very grateful for suggestions and feedback on how we can improve and develop J.UCS in the future. We also greatly appreciate the generous support of the J.UCS community, especially in promoting the journal and citing relevant articles in their research papers.  In this regular issue, I am very pleased to present 5 accepted articles by 16 authors from 7 different countries, namely Algeria, China, India, Ireland, Spain, Tunisia, and Turkiye.  Emre Önal and Abdullah Bülbül from Turkiye cover in their research a computational game unit balancing approach based on game theory to make each game unit equally preferable against a uniform play strategy. Ruchika Malhotra and Madhukar Cherukuri from India look in their research into Software Defect Categorization (SDC) models and apply convolutional neural networks in their empirical study. Song Yu, Bugao Jiang, Danni Zhang, and Zhifang Liao from China address in their research a cross-community question relevance prediction model, CCQRP, to predict the relevance of Stack Overflow questions and GitHub issues and recommend relevant GitHub issues. In a collaboration between researchers from Algeria and Tunesia, Soraya Setti Ahmed, Yahya Slimani and Riadh Frefita report their research on a fault tolerance model for the Hadoop Distributed System. In a collaboration between researchers form Spain and Ireland, Francisco Dominguez-Mateos, Vincent O’Brien, James Garland, Ryan Furlong, and Daniel Palacios-Alonso present their research on zero-shot learning for sub-discrimination in pre-trained models to differentiate several attributes such as gender, age, and skin tone, without any additional training. Enjoy Reading! Best wishes, Christian Gütl, Managing Editor-in-Chief Graz University of Technology, Graz, Austria

Automated Recommendation of Extracting Local Variable Refactorings

Extracting local variable refactoring is frequently employed to replace one or more occurrences of a complex expression with simple accesses to a newly introduced variable. To facilitate the refactoring, most IDEs can automate the extract local variable refactorings when the to-be-extracted expressions are selected by developers. However, refactoring tools usually replace all expressions that are lexically identical to the selected one without a comprehensive analysis of the safety of the refactoring. The automatically conducted refactorings may lead to serious software defects. Besides that, existing refactoring tools rely heavily on software developers to spot to-be-extracted expressions although it is often challenging for inexperienced developers and maintainers to make the selection. To this end, in this paper, we propose an automated approach, called ValExtractor+ , to recommending extract local variable refactoring opportunities and to automatically and safely conduct the refactorings. ValExtractor+ is composed of two parts, i.e., solutionAdvisor and opportunityAdvisor. Given a to-be-extracted expression, solutionAdvisor leverages lightweight static source code analysis to validate potential side effects of the expression, and to identify expressions that could be extracted together with the selected expression as a single variable without changing the semantics of the program or introducing any new exceptions. The static code analysis significantly improves the safety in automated extraction of local variables. To free programmers from manually selecting to-be-extracted expressions, opportunityAdvisor leverages solutionAdvisor to automatically retrieve all expressions that could be extracted safely as well as their refactoring solutions. It then leverages a learning-based classifier to predict which of the retrieved expressions should be extracted. Evaluations on open-source applications suggest that solutionAdvisor successfully avoided all defects (more than two hundred) caused by extracting local variable refactorings conducted by Eclipse (243 defects) or IntelliJ IDEA (263 defects). Additionally, opportunityAdvisor was able to effectively recommend expressions for extraction, achieving 307 true positives (TP) and 21,121 true negatives (TN). Four pull requests from our work (PR IDs: 66 , 333 , 439 , and 360 ) were successfully merged into the Eclipse community repository, showcasing the practical impact and robustness of our approach as recognized by the wider developer community.

RESEARCH NOTES—GMRepair: Graph Mining Template-Based Automated Software Repair

With the increasing scale and complexity of software recently, automated software bug repair has grown in importance. However, the current automated software bug repair process suffers from issues such as coarse-grained repair granularity and poor patch quality. To address these problems, we propose a graph mining template-based automatic software repair (GMRepair) to improve the performance of automated software bug repair. First, this approach adopts the Ochiai fault localization technique to locate and generate a list of suspicious defect statements. We utilize the GumTree tool to parse the bug and repair program files, generating edit scripts. These edit scripts are then transformed into a graphical representation. Second, we utilize a frequent graph miner to obtain graph mining templates by matching the context of the suspicious statements with the context of the graph mining templates, generating an initial population for them. The buggy program is evolved using genetic programming through mutation and crossover operations, generating new individuals. Finally, we sequentially pass the candidate patches (CPs) through corresponding test cases and prioritize the test cases using priority sorting techniques. Patches that fail to pass the test cases are filtered out, and the patches that pass the test cases are output. We conducted the experiments using two datasets, QuixBugs and Defects4J. In Defects4J, the GMRepair successfully repaired 41 defects, while in QuixBugs, it successfully repaired 15 defects. Compared to the existing methods, GMRepair offers a higher success rate and efficiency in defect repair.

Optimal frequency bands for pupillography for maximal correlation with HRV

Assessing cognitive load using pupillography frequency features presents a persistent challenge due to the lack of consensus on optimal frequency limits. This study aims to address this challenge by exploring pupillography frequency bands and seeking clarity in defining the most effective ranges for cognitive load assessment. From a controlled experiment involving 21 programmers performing software bug inspection, our study pinpoints the optimal low-frequency (0.06-0.29 Hz) and high-frequency (0.29-0.49 Hz) bands. Correlation analysis yielded a geometric mean of 0.238 compared to Heart Rate Variability features, with individual correlations for low-frequency, high-frequency, and their ratio at 0.279, 0.168, and 0.286, respectively. Extending the study to 51 participants, including a different experiment focusing on mental arithmetic tasks, validated the previous findings and further refined bands, maintaining effectiveness with a geometric mean correlation of 0.236 and surpassing common frequency bands reported in the existing literature. This study represents a pivotal step toward converging and establishing a coherent framework for frequency band definition to be used in pupillography analysis. Furthermore, based on this, it also contributes insights into the importance of more integration and adoption of eye-tracking with pupillography technology into authentic software development contexts for cognitive load assessment at a very fine level of granularity.

Feature Engineering of Sentiment Polarities for Bias/Variance Tradeoff Consideration in Software Defect Prediction Modelling

Feature Engineering of Sentiment Polarities for Bias/Variance Tradeoff Consideration in Software Defect Prediction Modelling

System Integration Failures and Their Impact on Patient Safety in Critical Care Settings

Robotic surgery systems offer precision and minimally invasive procedures, enhancing surgical outcomes in critical care settings. However, these systems' complexity, with integrated hardware and software, can lead to integration failures, compromising patient safety. This paper examines the system integration challenges in robotic surgery, explores their effects on patient safety, and suggests solutions to overcome them. Rigorous testing strategies during development and continuous monitoring of system performance are crucial in detecting failures early and ensuring reliable system operation. Keywords: Robotic Surgery Systems, System Integration, Patient Safety, Software Failures, Hardware Failures, Critical Care, Integration Testing, Failover Mechanisms, Robotic Arms, Sensor Calibration, Real-Time Data Processing, Medical Robotics, Surgical Precision, Failure Detection, Stress Testing, Fault-Tolerant Design, Error Handling, Modular Design, Quality Assurance, ISO 13485, Robotic Surgery Failures, Calibration Errors, Mako System, Da Vinci Surgical System, CyberKnife System, Automated Testing, Robotic Surgery Technology.

Adaptive Ensemble Learning Model-Based Binary White Shark Optimizer for Software Defect Classification

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.

An Ensemble Voting classification Approach for Software defects prediction

An Ensemble Voting classification Approach for Software defects prediction

Ensemble-Based Machine Learning Algorithms Combined with Near Miss Method for Software Bug Prediction

Abstract Software bug prediction (SBP) involves identifying or categorizing software modules likely to contain defects, utilizing underlying system properties such as software metrics. SBP plays a crucial role in enhancing software project quality and mitigating maintenance risks. Numerous machine learning (ML) algorithms have been developed to predict software bugs. Class imbalance poses a significant challenge for these algorithms, significantly impeding their effectiveness and resulting in imbalanced false-positive and false-negative outcomes. However, limited research has been conducted to specifically tackle the issue of class imbalance in the context of SBP. This study investigates the prediction performance of a homogeneous ensemble: Bagging, boosting, and voting classifiers (VC) methods combined with the under-sampling methods to address the class imbalance problem and improve the accuracy of SBP. Two ensembles are classified as bagging ensembles: decision tree (DT) and random forest (RF); two ensembles are classified as boosting ensembles: AdaBoost (AB) and gradient boosting (GB), while the DT, RF, K-Nearest Neighbours (K-NN), and support vector machine (SVM) are considered as VC. To establish the effectiveness of the proposed models, the experiments were conducted on the available benchmark datasets, which comprise five public datasets based on both class and file-level metrics. We compared and evaluated the performance of the proposed models according to several performance measures, namely accuracy, precision, recall, f-measure, Matthew’s correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUROC). The experimental findings demonstrated that the proposed models exhibit superior efficiency in predicting software bugs on balanced datasets compared to the original datasets, with an improvement of up to 11% accuracy for the class-level metrics and 10% for the file-level metrics. The results indicate that the use of data sampling techniques had a positive impact on the prediction accuracy of the presented models. We compared our proposed method with existing SBP methods based on several standard performance measures. The comparison outcomes revealed a significant superiority of our method over the prevailing state-of-the-art SBP methods across most datasets.

Software defect prediction based on multi-filter wrapper feature selection and deep neural network with attention mechanism

Software defect prediction based on multi-filter wrapper feature selection and deep neural network with attention mechanism

A comparative study of handling imbalanced data using generative adversarial networks for machine learning based software fault prediction

A comparative study of handling imbalanced data using generative adversarial networks for machine learning based software fault prediction

Safety-Critical Software Failure Prevention Using Defence-in-Depth Approach

Safety-Critical Software Failure Prevention Using Defence-in-Depth Approach

EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection.

When developing general-purpose robot software components, we often lack complete knowledge of the specific contexts in which they will be executed. This limits our ability to make predictions, including our ability to detect program bugs statically. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loop or even cause safety hazards. This paper proposes an approach to help developers catch these errors as soon as we have some context (typically at pre-launch time) with minimal additional efforts. We use embedded domain-specific language (DSL) techniques to enforce early checks. We describe design patterns suitable for robot programming and show how to use these design patterns for DSL embedding in Python, using two case studies on an open-source robot skill platform SkiROS2, designed for the composition of robot skills. These two case studies help us understand how to use DSL embedding on two abstraction levels: the high-level skill description that focuses on what the robot can do and under what circumstances and the lower-level decision-making and execution flow of tasks. Using our DSL EzSkiROS, we show how our design patterns enable robotics software platforms to detect bugs in the high-level contracts between the robot's capabilities and the robot's understanding of the world. We also apply the same techniques to detect bugs in the lower-level implementation code, such as writing behavior trees (BTs), to control the robot's behavior based on its capabilities. We perform consistency checks during the code deployment phase, significantly earlier than the typical runtime checks. This enhances the overall safety by identifying potential issues with the skill execution before they can impact robot behavior. An initial study with SkiROS2 developers shows that our DSL-based approach is useful for finding bugs early and thus improving the maintainability of the code.

Adaptive cat optimization with Attention based bi-LSTM for automatic software bug detection

Adaptive cat optimization with Attention based bi-LSTM for automatic software bug detection

Comparative Analysis of Machine Learning Model and PSO Optimized CNN-RNN for Software Fault Prediction

Comparative Analysis of Machine Learning Model and PSO Optimized CNN-RNN for Software Fault Prediction

Current Trends in Class Imbalance Learning for Software Defect Prediction

Current Trends in Class Imbalance Learning for Software Defect Prediction

Fed-OLF: Federated Oversampling Learning Framework for Imbalanced Software Defect Prediction Under Privacy Protection

Fed-OLF: Federated Oversampling Learning Framework for Imbalanced Software Defect Prediction Under Privacy Protection

Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction: Datasets, Validation Methodologies, Prediction Approaches, and Tools

Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction: Datasets, Validation Methodologies, Prediction Approaches, and Tools

Bug Report Analytics for Software Reliability Assessment using Hybrid Swarm – Evolutionary Algorithm

Background: With the growing advances in the digital world, software development demands are increasing at an exponential rate. To ensure reliability of the software, high-performance tools for bug report analysis are needed. Aim: This paper proposes a new ‘Iterative Software Reliability’ model based on one of the most recent Software Development Life Cycle (SDLC) approach. Method: The proposed iterative failure rate model assumes that new functionality enhancement occurs in each iteration of software development and accordingly design modification is made at each stage of software development. In terms of defects, testing effort, and added functionality, these changing needs in each iteration are reflected in the proposed model using iterative factors. The proposed model has been tested on twelve Eclipse and six JDT software failure datasets. Proposed model parameters have been estimated using a hybrid swarm - evolutionary algorithm. Results: The proposed model has about 32% and 55% improved efficiency on Eclipse and JDT datasets respectively as compared to other models like Jelinski Moranda Model, Shick-Wolverton Model, Goel Okumotto Imperfect Model etc. Conclusion: In each analysis done, the proposed model is found to be reaching acceptable performance and could be applied on other software failure datasets for further validation.