Software Fault Detection Research Articles

The use of artificial intelligence for automatic analysis and reporting of software defects

The COVID-19 pandemic marked a before and after in the business world, causing a growing demand for applications that streamline operations, reduce delivery times and costs, and improve the quality of products. In this context, artificial intelligence (AI) has taken a relevant role in improving these processes, since it incorporates mathematical models that allow analyzing the logical structure of the systems to detect and reduce errors or failures in real-time. This study aimed to determine the most relevant aspects to be considered for detecting software defects using AI. The methodology used was qualitative, with an exploratory, descriptive, and non-experimental approach. The technique involved a documentary review of 79 bibliometric references. The most relevant finding was the use of regression testing techniques and automated log files, in machine learning (ML) and robotic process automation (RPA) environments. These techniques help reduce the time required to identify failures, thereby enhancing efficiency and effectiveness in the lifecycle of applications. In conclusion, companies that incorporate AI algorithms will be able to include an agile model in their lifecycle, as they will reduce the rate of failures, errors, and breakdowns allowing cost savings, and ensuring quality.

A Brief Analysis of the Progress and Trends in Software Defect Prediction Methods

Software defect detection is particularly important for modern society, as it is a crucial step in ensuring the quality and reliability of software systems. With the emergence of artificial intelligence (AI), research in software defect detection has evolved from traditional methods to more complex approaches that utilize deep learning and large language models (LLMs). The advent of LLMs has fundamentally changed the paradigm of software development and defect detection, bringing new challenges and confusion to the field of software defect prediction research. To address these issues, we compare software defect detection methods based on traditional techniques, deep learning approaches, and LLMs through a literature review. We analyze the changes brought about by the introduction of LLMs to software development and propose new insights. Additionally, we examine the progress and trends in software defect prediction to provide inspiration for subsequent research.

Optimization of Backward Elimination for Software Defect Prediction with Correlation Coefficient Filter Method

Detecting software defects is a crucial step for software development not only to reduce cost and save time, but also to mitigate more costly losses. Backward Elimination is one method for detecting software defects. Notably Backward Elimination may remove features that may later become significant to the outcome affecting the performance of Backward Elimination. The aim of this study is to improve Backward Elimination performance. In this study, several features were selected based on their correlation coefficient, with the selected feature applied to improve Backward Elimination final model performance. The final model was validated using cross validation with Naïve Bayes as the classification method on the NASA MDP dataset to determine the accuracy and Area Under the Curve (AUC) of the final model. Using top 10 correlation feature and Backward Elimination achieve an average result of 86.6% accuracy and 0.797 AUC, while using top 20 correlation feature and Backward Elimination achieved an average result of 84% accuracy and 0.812 AUC. Compare to using Backward Elimination and Naïve Bayes respectively the improvement using top 10 correlation feature as follows: AUC:1.52%, 13.53% and Accuracy: 13%, 12.4% while the improvement using top 20 correlation feature as follows: AUC:3.43%, 15.66% and Accuracy: 10.4%, 9.8%. Results showed that selecting the top 10 and top 20 feature based on its correlation before using Backward Elimination have better result than only using Backward Elimination. This result shows that combining Backward Elimination with correlation coefficient feature selection does improve Backward Elimination’s final model and yielding good results for detecting software defects.

Comparative Analysis of Machine Learning Models for Enhanced Software Fault Detection: Performance and Accuracy Evaluation

Software defect prediction expectation is a critical portion of program designing that tries to discover and anticipate bugs or imperfections in program frameworks some time recently they happen. The objective is to come up with strategies and models that offer assistance program advancement groups choose which testing assignments are most imperative and how to best utilize their assets. A few machine learning (ML) strategies are utilized to do this. To create forecasts, these frameworks utilize a parcel of distinctive information, like code measures, past imperfection information, and data approximately the coder. The reason of this study is to use different machine learning strategies to make a determining demonstrate for finding computer program bugs. The proposed demonstrate is utilized in tests on the KC2 dataset from NASA's Guarantee library as portion of the study. It was attempted and compared how well diverse machine learning strategies worked. This consider utilized the KC2 dataset and found that the Decision Tree strategy got 73.28% accuracy, Naïve Bayes got 85.96%, K-Nearest Neighbour (KNN) got 82.57%, Support Vector Machine (SVM) got 86.74%, and Random Forest got 84.20%. The study about appeared that these different models can precisely anticipate program bugs when the Guarantee dataset KC2 is utilized. These comes about appear that machine learning models can offer assistance anticipate bugs superior, which can offer assistance groups speed up their tests and make way better utilize of their assets. The study appears that employing a run of machine learning models can enormously move forward the precision of foreseeing computer program abandons. This gives us valuable data almost regions of program frameworks that are more likely to have bugs, which helps keep software quality high.

Improving model performance for software defect detection and prediction using ensemble method and cross validation techniques

Improving model performance for software defect detection and prediction using ensemble method and cross validation techniques

FSBOA: feature selection using bat optimization algorithm for software fault detection

Feature selection (FS) plays a crucial role in software fault prediction (SFP), aiming to identify a subset of relevant and discriminative features from a large pool of software metrics. It serves as a critical preprocessing step in building accurate fault prediction models, enabling the identification of potential software faults early in the development life cycle and facilitating effective resource allocation for testing and maintenance activities. The study's objective is to determine how well the bat optimization algorithm (BOA) can extract the features that are most important for correctly predicting software flaws, improve the accuracy of fault prediction, reduce the dimensionality of the feature space, and mitigate the risk of overfitting, thereby enabling more effective resource utilization and better allocation of testing efforts. The forecasting models underwent testing and training utilizing a collection of software metrics, with the datasets undergoing evaluation using several different FS algorithms. An assessment was conducted by contrasting the effectiveness of multiple optimization algorithms, including evolutionary methods such as FS employing genetic algorithm (FSGA), FS employing differential evolution (FSDE), and swarm-based techniques such as FS employing ant colony optimization (FSACO), FS employing particle swarm optimization (FSPSO), FS employing firefly algorithm (FSFA), and FS employing binary grey wolf optimization algorithm (FSBGWO) in relation to FS employing bat optimization algorithm (FSBAO). The results obtained from FSBAO approach demonstrate the effectiveness in solving FS optimization problems with at most accuracy of 98.92%. Furthermore, the experimental results have been statistically validated for the greater efficiency of the proposed FSBAO algorithm. This study's findings have crucial implications for developing a software failure prediction models that is more accurate and efficient.

Modeling Reliability-Driven Software Release Strategy Considering Testing Effort with Fault Detection and Correction Processes: A Control Theoretic Approach

In this paper, we have introduced a software reliability growth model (SRGM) that integrates a Weibull testing effort function (TEF) within the software fault detection process (FDP) and fault correction process (FCP), effectively capturing the intricacies of software testing. Leveraging the least squared estimation method, we estimated both model parameters and the TEF, leading to a notably improved fit with the dataset and enhancing our comprehension of software reliability dynamics. Our approach also involved employing a reliability-based method to identify the optimal time for software release, further reinforcing the robustness of our model. Subsequently, we devised an optimal control model to minimize testing efforts throughout the software development life cycle. This work provides a comprehensive methodological framework for computing the total cost while ensuring debugging costs follow a learning curve pattern. Through numerical simulations involving hypothetical values and the Weibull TEF, we observed that once the software achieves the desired reliability for release, subsequent costs gradually decline to zero. In essence, both testing and future costs converge to zero post-release, reducing instantaneous expenses.

Parameter-Efficient Multi-classification Software Defect Detection Method Based on Pre-trained LLMs

Software Defect Detection (SDD) has always been critical to the development life cycle. A stable defect detection system can not only alleviate the workload of software testers but also enhance the overall efficiency of software development. Researchers have recently proposed various artificial intelligence-based SDD methods and achieved significant advancements. However, these methods still exhibit limitations in terms of reliability and usability. Therefore, we introduce MSDD-(IA)3, a novel framework leveraging the pre-trained CodeT5+ and (IA)3 for parameter-efficient multi-classification SDD. This framework constructs a detection model based on pre-trained CodeT5+ to generate code representations while capturing defect-prone features. Considering the high overhead of pre-trained LLMs, we injects (IA)3 vectors into specific layers, where only these injected parameters are updated to reduce the training cost. Furthermore, leveraging the properties of the pre-trained CodeT5+, we design a novel feature sequence that enriches the input data through the combination of source code with Natural Language (NL)-based expert metrics. Our experimental results on 64K real-world Python snippets show that MSDD-(IA)3 demonstrates superior performance compared to state-of-the-art SDD methods, including PM2-CNN, in terms of F1-weighted, Recall-weighted, Precision-weighted, and Matthews Correlation Coefficient. Notably, the training parameters of MSDD-(IA)3 are only 0.04% of those of the original CodeT5+. Our experimental data and code can be available at (https://gitee.com/wxyzjp123/msdd-ia3/).

Development of a Novel Classifier Model Implementing Bio-inspired Algorithms to Optimize Existing Models for Software Defect Detection

Development of a Novel Classifier Model Implementing Bio-inspired Algorithms to Optimize Existing Models for Software Defect Detection

Online boxplot derived outlier detection

AbstractOutlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud detection, and real-time monitoring systems. Detecting outliers in real time is crucial in several industries, such as financial fraud detection and quality control in manufacturing processes. In the context of big data, the amount of data generated is enormous, and traditional batch mode methods are not practical since the entire dataset is not available. The limited computational resources further compound this issue. Boxplot is a widely used batch mode algorithm for outlier detection that involves several derivations. However, the lack of an incremental closed form for statistical calculations during boxplot construction poses considerable challenges for its application within the realm of big data. We propose an incremental/online version of the boxplot algorithm to address these challenges. Our proposed algorithm is based on an approximation approach that involves numerical integration of the histogram and calculation of the cumulative distribution function. This approach is independent of the dataset’s distribution, making it effective for all types of distributions, whether skewed or not. To assess the efficacy of the proposed algorithm, we conducted tests using simulated datasets featuring varying degrees of skewness. Additionally, we applied the algorithm to a real-world dataset concerning software fault detection, which posed a considerable challenge. The experimental results underscored the robust performance of our proposed algorithm, highlighting its efficacy comparable to batch mode methods that access the entire dataset. Our online boxplot method, leveraging dataset distribution to define whiskers, consistently achieved exceptional outlier detection results. Notably, our algorithm demonstrated computational efficiency, maintaining constant memory usage with minimal hyperparameter tuning.

A clustering-based resampling technique with cluster structure analysis for software defect detection in imbalanced datasets

Software defect detection focuses on the automatic identification of flaws in software modules. Given the great importance of the problem, numerous researchers have introduced a rich collection of deep learning approaches to confront it. However, the datasets that are used to train the proposed classifiers are in most cases highly imbalanced, leading to models that cannot learn the minority classes effectively, while being biased towards the majority class. The state-of-the-art solutions either overlook the issue of data imbalance, or they confront it insufficiently by ignoring the existence of outliers and the local properties of the classes' distributions. In this work we introduce CBR, a Clustering-Based Resampling technique for mitigating the problem of class imbalance in software defect detection tasks. The proposed method initially employs a quite simple heuristic to determine the maximum distance threshold between two clusters. Then, it uses this threshold to apply hierarchical clustering with the aim of grouping together similar samples. CBR considers the singleton clusters as outliers, and discards the ones originating from the majority class. The algorithm subsequently organizes the clusters into sub-clusters than contain samples from the same class and determines which sub-clusters should participate in the oversampling process. In this way, CBR produces samples of improved quality and variance. We evaluated the performance of CBR against 9 baseline and state-of-the-art techniques by using 27 datasets and a Multilayer Perceptron classifier. The results demonstrate the superiority of CBR in terms of Balanced Accuracy and Precision scores.

Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes.

An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

AbstractOf late, the Internet of Things (IoT) has progressed in its pervasiveness across the globe for diverse applications. Wireless Sensor Network (WSN) is one of the prominent technologies employed in IoT environments where multiple tiny sensor nodes are distributed to sense real‐time observations about unforeseeable areas for control and managerial purposes. Owing to the presence of sensors in inaccessible regions and their battery restrictions, different types of software faults occur in IoT‐enabled WSNs (IWSNs). These faults create uncertainty in data reading which causes serious damage to the sensor network. Hence, the IWSN necessitates an effective fault‐detection methodology to continue optimal activity despite the existence of software faults. This work proposes a novel Energy‐Aware Hierarchical Rule‐based Software Fault Detection (HRSFD) model to identify various software faults with minimum energy depletion in the IWSN environment. Primarily, the proposed model extracts antecedent attributes from the characteristics of the sensed data. Its abnormal values can be identified based on the obtained antecedent attributes. Subsequently, the category of the software fault is determined by applying a hierarchical rule strategy. Finally, from the simulation results, it is apparent that the fault detection accuracy rate of the proposed HRSFD model attains 99.12% for dense networks. The lifetime of the network is also prolonged by 18% as compared to the existing state‐of‐the‐art models.

Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection

AbstractSoftware defect detection (SDD) is crucial to ensure the reliability of software systems and identify defects in classification. One of the key challenges in defect detection is to select more informative and relevant features from the vast pool of available software metrics. A novel approach is proposed in this paper that leverages mutation boosted salp swarm optimizer (MBSSO) and Rough Set Theory for feature selection (FS) in SDD. It efficiently explores the search space and incorporates a mutation boosting mechanism to overcome local optima. Rough Set Theory provides a formal framework for analyzing feature relevance and dependency, while MBSSO optimizes the search for the optimal feature subset. The proposed approach involves encoding the feature subsets using binary representation and designing a fitness function. The MBSSO algorithm explores the feature space, iteratively improving the feature subset based on the fitness function. The selected feature subset is then used to train a defect detection model using the Kernel Extreme Learning Machine (KELM) algorithm. The experimental validation is performed by Project Repository for Software Engineering (PROMISE) dataset and compared the performance of proposed approach against other FS methods and baselines. The experimental validation demonstrates that the proposed approach achieves superior performance and selected feature subset improves the accuracy and efficiency. This research contributes to the advancement of SDD by providing an effective and efficient FS technique using MBSSO and Rough Set Theory.

An empirical analysis of feature selection techniques for Software Defect Prediction

<p>Detecting software defects before they occur is crucial in software engineering as it impacts software system quality and reliability. Previous studies on predicting software defects have typically employed software features, such as code size, complexity, coupling, cohesion, inheritance, and other software metrics., to forecast whether a code file or commit is prone to defects in the future. However, it is advantageous to restrict the number of features employed in a defect prediction model to avoid the challenges associated with multicollinearity and the “curse of dimensionality” and to simplify the data analysis process. By using a reduced number of features, the defect prediction model can concentrate on the most significant variables and improve its accuracy. This research paper investigates the impact of eight feature selection methods on the accuracy and stability of six supervised learning models. This study is novel as it is based on exhaustive experimentation of each of the eight feature selection techniques with each of the six supervised learning models. Two notable findings have been obtained. First, we discovered that the association and coherence-based techniques have demonstrated the highest level of accuracy when it comes to defect prediction. The models that utilized these selected features outperformed those using the original features. Second, the feature selection techniques, namely Correlation feature selection, Recursive feature elimination, and Ridge feature selection when combined with the Support vector machine and Decision tree classifier, consistently selected low-variance features across multiple supervised defect prediction models. When combined with different classifiers, these techniques achieved exceptional performance on the publicly available NASA datasets CM1 and PC2. The findings revealed a remarkable accuracy rate of over 85% for CM1 and 95% for PC2, accompanied by precision, recall, and f-measure values exceeding 95%. These exceptional results indicate the achievement of the highest level of performance in the evaluation.</p>

Software Fault Prediction Using FeatBoost Feature Selection Algorithm

A critical aspect of software engineering is Software fault prediction which aims to identify and prevent errors in software systems before their release which can cause failures or issues for its users. Various techniques and tools have been developed to detect software faults, including static code analysis, dynamic testing, and machine learning-based approaches. In past few years, the world has seen a growing interest in the use of ML models for predicting software faults, as they can effectively analyse high dimensional datasets and detect complex patterns which are difficult for human experts to detect. However, developing accurate and reliable software fault detection models requires careful selection of data, feature engineering, and model evaluation. This purpose of this paper is to present a comprehensive analysis of potential applications and future research directions in the field of software fault detection. The study emphasizes the importance of identifying and addressing software faults to ensure the reliability and efficiency of software systems. Additionally, the paper outlines various approaches and techniques that can be employed for effective software fault detection.

Explainable Software Defects Classification Using SMOTE and Machine Learning

Software defect prediction is a critical task in software engineering that aims to identify and mitigate potential defects in software systems. In recent years, numerous techniques and approaches have been developed to improve the accuracy and efficiency of the defect prediction model. In this research paper, we proposed a comprehensive approach that addresses class imbalance by utilizing stratified splitting, explainable AI techniques, and a hybrid machine learning algorithm. To mitigate the impact of class imbalance, we employed stratified splitting during the training and evaluation phases. This method ensures that the class distribution is maintained in both the training and testing sets, enabling the model to learn from and generalize to the minority class examples effectively. Furthermore, we leveraged explainable AI methods, Lime and Shap, to enhance interpretability in the machine learning models. To improve prediction accuracy, we propose a hybrid machine learning algorithm that combines the strength of multiple models. This hybridization allows us to exploit the strength of each model, resulting in improved overall performance. The experiment is evaluated using the NASA-MD datasets. The result revealed that handling the class imbalanced data using stratify splitting approach achieves a better overall performance than the SMOTE approach in Software Defect Detection (SDD).

Detecting system defects of digital devices in the course of malfunction imitation using fuzzing

Aim. The paper proposes approaches to the organisation of testing of digital systems through malfunction imitation for the purpose of ensuring compliance of international and Russian failure and fault resistance standards for the purpose of efficient (in terms of time) detection of software defects as part of mass production of products. The paper proposes a structure and operating algorithm of a hardware and software test bed for malfunction simulation intended for testing a system’s devices. The test bed collects and processes data for fuzzing, hardware error identification, as well as defines the scope of testing.Methods. The paper used basic systems approach, classical methods of the probability theory and mathematical statistics, decision theory, methods of hardware and software testing and development, mathematical theory of fuzzy sets and fuzzy logic.Results. Malfunction simulation algorithms were developed for the purpose of testing hardware and software systems using fuzzing that ensure probabilistic estimation of the termination time of testing with a specified accuracy.Conclusions. The above set of algorithms allows detecting system defects in the process of software and hardware integration into a single system that cause new malfunctions (emergence) that cannot be taken into consideration at the design stage.

Imperfect Debugging-Based Generalized Exponential Testing Effort Estimation for Software Fault Detection and Removal

In the current technological era, the reliability growth model depicts the failure in software that is employed for estimating the reliability of software. Conventional software reliability growth model (SRGMs) usually presume that faults in software are immediately corrected once a software fault is found and no new faults are introduced. This consideration can be impractical. However, introduction of several new faults is possible throughout debugging, which is called imperfect debugging. During such a process of debugging and introduction of new faults, consumption of testing effort also plays an important role. Hence, in this paper, two models are proposed that incorporate testing effort along with imperfect debugging during fault correction. Additionally, the combined testing effort is the addition of two efforts, specifically detection effort as well as correction effort. The formulated models are validated for real data set and contrasted with additional famous SRGMs. These inferences indicate that Model 2 performs best in terms of fitting and prediction capability.

Quality Analysis of Software Applications using Software Reliability Growth Models and Deep Learning Models

Finding the faults in the software is a very tedious task. Many software companies are trying to develop high-quality software which is having no faults. It is very important to analyze the errors, faults, and bugs in software development. Software reliability growth models (SRGM's) are used to help the software industries to create quality software products. Quality is the software metric that is used to analyze the performance of the software product. The software product which is having no errors or faults is considered the best software product. SRGM is also utilized to analyze the software quality based on the programming language. Deep Learning (DL) is a sub-domain in machine learning to solve several complex issues in software development. Finding accurate patterns from software faults is a very tedious task. DL algorithm performs better in integrating the SRGM with the DL approaches giving better results based on software fault detection. Many software faults real-time datasets are available to analyze the DL approaches. The performances of the various integrated models are analyzed by showing the quality metrics.