African buffalo optimized multinomial softmax regression based convolutional deep neural network for software fault prediction

Software defects and quality assurance are crucial aspects of software development that should be considered during the software development cycle. To ensure high-quality software, it is essential to have a robust quality assurance process in place. System reliability and quality are very key components that must be considered during software development, and this can only be achieved when software undergoes a thorough test process for errors, anomalies, defects, omissions, and bugs. Early software defect prediction and detection play an essential role in ensuring the reliability and quality of software systems, ensuring that software companies discover errors or defects early enough and allocate more resources to defect-prone modules. This study proposes the development of an enhanced classifier model for software defect prediction and detection. The aim is to harness the collective intelligence of selected base classifiers like Support Vector Machine, Logistic regression, Decision Trees, Random Forest, AdaBoost, Gradient Boosting, K-Nearest Neighbor, GaussianNB, and Multi-Layer Perception to improve accuracy, robustness, and generalization in identifying potential defects using a soft voting ensemble technique. The ensemble model leveraged the confidence probability of the soft voting technique and the generalization advantage of cross-validation leading to a more robust and dynamic model. The performance of the model with existing classifiers was evaluated using accuracy, F1 score, Precision, and area under the ROC curve (ROC- AUC) as the evaluation metrics. The results of the experiment revealed that the Proposed Classifier produced an overall Accuracy rate of 93%, and ROC AUC of 98%. The results demonstrate the effectiveness of our enhanced ensemble classifier in software defect detection and prediction. By harnessing the strengths of diverse base classifiers, our approach provides a robust and adaptive solution to the challenges of early detection and mitigating defects in software systems. This research contributes to the advancement of reliable software development practices and lays the foundation for future enhancements in ensemble-based defect detection methodologies.

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.

Software Defect Prediction (SDP) plays an active area in many research domain of Software Quality of Assurance (SQA). Many existing research studies are based on software traditional metric sets and defect prediction models are built in machine language to detect the bug for limited source code line. Inspired by the above existing system. In this paper, defect prediction is focused on predicting defects in source code. The objective of this thesis is to improve the software quality for accurate defect prediction is source code for file level. So, that it helps the developer to find the bug and fix the issue, to make better use of a resource which reduces the test effort, minimize the cost and improve the quality of software. A new approach is introduced to improve the prediction performance of Bidirectional RNNLM in Deep Neural Network. To build the defect prediction model a defect learner framework is proposed and first it need to build a Neural Language Model. Using this Language Model it helps to learn to deep semantic features in source code and it train & test the model. Based on language model it combined with software traditional metric sets to measure the code and find the defect. The probability of language model and metric set Cross-Entropy with Abstract Syntax Tree (CE-AST) metric is used to evaluate the defect proneness and set as a metric label. For classification the metric label K-NN classifier is used. BPTT algorithm for learning RNN will provide additional improvement, it improves the predictions performance to find the dynamic error.

Analysing the Adaptability of Software Defect Prediction in Small Software Firms

The main aim of software fault prediction is the identification of such classes and methods where faults are expecting at an early stage using some properties of the project. Early-stage prediction of software faults supports software quality assurance activities. Evaluation of code smells for anticipating software faults is basic to ensure its importance in the field of software quality. In this paper, we investigate the impact of code smells on software fault prediction at the class level and method level. Previous studies show the impact of code smells on fault prediction. However, using code smells for class level faults prediction and method level fault prediction need more concern. We use defects4j repository for the creation of datasets used in building software fault prediction model-based. We use pseudo labeling for class level prediction and bagging for method level prediction. We extract code smells from different classes and methods and then used these extracted code smells for fault prediction. We compare our prediction results with actual results and see if our prediction is correct in order to do validation.

Estimating the quality of software modules provides a practical way to assess their potential risk. A software quality estimation can be used to provide a targeted software quality improvement initiative. The aim is to provide the management team with a practical quality assessment of software modules for a cost‐effective utilization of software quality improvement resources. In our previous and ongoing research, we have empirically investigated three different kinds of software quality models that are built using software measurement data—software quality classification, software fault prediction, and module‐order modeling. The classification model predicts the class membership of software modules into the fault‐prone and not fault‐prone classes. The fault prediction model estimates the number of faults expected in the modules. A module‐order model predicts the ranking of the modules with respect to their relative software quality. The preference among the three software quality models depends on the software quality improvement goals of the given software project team. We present the useful principles and a detailed overview of the three software quality estimation models, using a case study of a large‐scale telecommunications software system. The classification model is built using the logistic regression technique, whereas the fault prediction model is built using the multiple linear regression technique. The module‐order model is built using the estimations of the fault prediction model for ranking the software modules. We summarize our work with suggestions for software tools that can be used to build software quality estimation models.

Predicting parts of the software programs that are more defects prone could ease up the software testing process and helps effectively to reduce the cost and time of developments. Although many machine-learning and statistical techniques have been proposed widely for defining fault prone modules in software fault prediction, but this area have yet to be explored with high accuracy and less error. Unfortunately, several earlier methods including artificial neural networks and its variants that have been used, marred by limitations such as inability to adequately handle uncertainties in software measurement data which leads to low accuracy, instability and inconsistency in prediction. In this paper, first the effect of irrelevant and inconsistent modules on fault prediction is decreased by designing a new framework, in which the entire project’s modules are clustered. The generated output is then passed to the next model in the hybrid setting, which is a probabilistic neural network (PNN) for training and prediction. We used four NASA data sets to evaluate our results. Performance evaluation in terms of false positive rate, false negative rate, and overall error are calculated and showed 30% to 60% improvement in false negative rate compared to other well-performed training methods such as naïve Bayes and random forest.KeywordsFuzzy ClusteringFault PredictionProbabilistic Neural Network

Recent studies of software defect prediction typically produce datasets, methods and frameworks which allow software engineers to focus on development activities in terms of defect-prone code, thereby improving software quality and making better use of resources. Many software defect prediction datasets, methods and frameworks are published disparate and complex, thus a comprehensive picture of the current state of defect prediction research that exists is missing. This literature review aims to identify and analyze the research trends, datasets, methods and frameworks used in software defect prediction research betweeen 2000 and 2013. Based on the defined inclusion and exclusion criteria, 71 software defect prediction studies published between January 2000 and December 2013 were remained and selected to be investigated further. This literature review has been undertaken as a systematic literature review. Systematic literature review is defined as a process of identifying, assessing, and interpreting all available research evidence with the purpose to provide answers for specific research questions. Analysis of the selected primary studies revealed that current software defect prediction research focuses on five topics and trends: estimation, association, classification, clustering and dataset analysis. The total distribution of defect prediction methods is as follows. 77.46% of the research studies are related to classification methods, 14.08% of the studies focused on estimation methods, and 1.41% of the studies concerned on clustering and association methods. In addition, 64.79% of the research studies used public datasets and 35.21% of the research studies used private datasets. Nineteen different methods have been applied to predict software defects. From the nineteen methods, seven most applied methods in software defect prediction are identified. Researchers proposed some techniques for improving the accuracy of machine learning classifier for software defect prediction by ensembling some machine learning methods, by using boosting algorithm, by adding feature selection and by using parameter optimization for some classifiers. The results of this research also identified three frameworks that are highly cited and therefore influential in the software defect prediction field. They are Menzies et al. Framework, Lessmann et al. Framework, and Song et al. Framework.

A fuzzy logic based approach for phase-wise software defects prediction using software metrics

WITHDRAWN: Software bug prediction and classification by global pooling of different activation of convolution layers

SummaryWith the development of smartphones, mobile applications play an irreplaceable role in our daily life, which characteristics often commit code changes to meet new requirements. This characteristic can introduce defects into the software. To provide immediate feedback to developers, previous researchers began to focus on just‐in‐time (JIT) software defect prediction techniques. JIT defect prediction aims to determine whether code commits will introduce defects into the software. It contains two scenarios, within‐project JIT defect prediction and cross‐project JIT defect prediction. Regardless of whether within‐project JIT defect prediction or cross‐project JIT defect prediction all need to have enough labeled data (within‐project JIT defect prediction assumes that have plenty of labeled data from the same project, while cross‐project JIT defect prediction assumes that have sufficient labeled data from source projects). However, in practice, both the source and target projects may only have limited labeled data. We propose the MTL‐DNN method based on multi‐task learning to solve this question. This method contains the data preprocessing layer, input layer, shared layers, task‐specific layers, and output layer. Where the common features of multiple related tasks are learned by sharing layers, and the unique features of each task are learned by the task‐specific layers. For verifying the effectiveness of the MTL‐DNN approach, we evaluate our method on 15 Android mobile apps. The experimental results show that our method significantly outperforms the state‐of‐the‐art single‐task deep learning and classical machine learning methods. This result shows that the MTL‐DNN method can effectively solve the problem of insufficient labeled training data for source and target projects.

Software defect prediction is an important issue in the process of software development and maintenance, which is related to the overall success or failure of software. This is because early software failure prediction can improve software quality, reliability and efficiency, and reduce software cost. However, developing robust defect prediction models is a challenging task and many techniques have been proposed in the literature. In this paper, a software defect prediction model based on Novel Hybrid Genetics Software Defect Prediction (NHGSDP) is proposed. The supervised NHGSDP algorithm has been used to predict future software failures based on historical data. The evaluation process shows that the NHGSDP algorithm can be used effectively with high accuracy. The collected results show that the NHGSDP method has better performance.

The primary goal of software quality engineering is to apply various techniques and processes to produce a high quality software product. One strategy is applying data mining techniques to software metrics and defect data collected during the software development process to identify the potential low-quality program modules. In this paper, we investigate the use of feature selection in the context of software quality estimation (also referred to as software defect prediction), where a classification model is used to predict program modules (instances) as fault-prone or not-fault-prone. Seven filter-based feature ranking techniques are examined. Among them, six are commonly used, and the other one, named signal to noise ratio (SNR), is rarely employed. The objective of the paper is to compare these seven techniques for various software data sets and assess their effectiveness for software quality modeling. A case study is performed on 16 software data sets and classification models are built with five different learners. Our experimental results are summarized based on statistical tests for significance. The main conclusion is that the SNR technique performs better than or similar to the best performer of the six commonly used techniques.

Machine learning (ML) is becoming increasingly important as a research tool due to its various frameworks and learning approaches. With the ever-increasing scale of software, reliability has become a crucial issue and software defect prediction is utilized to assist developers in finding potential defect and allocating their testing efforts. Traditional methods of software defect prediction mainly focus on designing static code metrics which are fed into ML classifiers to predict defects in the code. Even with the same ML techniques, many researchers apply statistical approaches to classify software modules and decide whether each module is defect prone or not and, accordingly, train their model. Deep neural network (DNN) and convolutional neural network (CNN) models built by the appropriate design decisions are crucial to obtain the desired classifier performance. This is especially significant when predicting fault proneness of software modules. When correctly identified, this could help in reducing the testing cost by directing the efforts more toward the modules identified to be fault prone. This paper proposed a N ovel CNN (NCNN) model to predict software defects. The framework used is Python Programming Language with Keras and TensorFlow. A comparative analysis with ML algorithms [such as Random Forest (RF), Decision Trees (DT), and Naïve Bayes (NB)] and DNN model in terms of F-measure (known as F1-score), recall, precision, and accuracy has been presented from four NASA system data sets (KC1, PC1, PC2, and KC3) selected from PROMISE repository. The experimental results indicated that NCNN model was comparable to the existing classifiers and outperformed them in most of the experiments.

Recent years, convolutional neural network (CNN) is a research hot spot in machine learning and has some application value in computer aided diagnosis. Firstly, this paper briefly introduces the basic principle of CNN. Secondly, it summarizes the improvement on network structure from two dimensions of model and structure optimization. In model structure, it summarizes eleven classical models about CNN in the past 60 years, and introduces its development process according to timeline. In structure optimization, the research progress is summarized from five aspects (input layer, convolution layer, down-sampling layer, full-connected layer and the whole network) of CNN. Thirdly, the learning algorithm is summarized from the optimization algorithm and fusion algorithm. In optimization algorithm, it combs the progress of the algorithm according to optimization purpose. In algorithm fusion, the improvement is summarized from five angles: input layer, convolution layer, down-sampling layer, full-connected layer and output layer. Finally, CNN is mapped into the medical image domain, and it is combined with computer aided diagnosis to explore its application in medical images. It is a good summary for CNN and has positive significance for the development of CNN.