JIT-DCK: A KAN Multi-Task Model for Just-In-Time Code Defect Prediction and Localization

Software testing and defect prediction are widely recognized quality assurance activities that can contribute to the effectiveness and efficiency of Just-In-Time Software Quality Assurance (JIT-SQA) by automating processes and reducing software maintenance costs. However, most studies treat them separately, with limited exploration of how defect prediction can guide testing and how testing can support prediction. Existing methods are not tailored for JIT-SQA and often ignore change-specific characteristics such as concept drift and verification latency, leading to unstable defect prediction. Moreover, test annotations on recent code changes are rarely used to update models, missing an opportunity to improve accuracy. Beyond this, effectively leveraging test-annotated samples remains an open research question. On the one hand, these samples frequently suffer from one-sided label noise, as instances that are not identified as defective during testing may still contain defects. On the other hand, defect-inducing changes are relatively infrequent, leading to a significant class imbalance issue. Therefore, exploring how to utilize test annotations to mitigate class imbalance and enhance prediction robustness presents a critical challenge. To fill this research gap, we propose an automatic JIT-SQA framework that incorporates defect prediction and testing processes collaboratively. Specifically, we direct testing activities through defect prediction models and design a novel update mechanism that integrates waiting time with testing feedback to update the predictor in real time. Moreover, we further design a reinforcement learning-based dynamic selection mechanism to mitigate the instability in defect prediction performance over time. Our framework facilitates mutual guidance between automatic testing and defect prediction, leveraging shared knowledge within the JIT-SQA system. This integration holds significant potential for producing high-quality software within modern development timelines. Notably, this study is the first to investigate the integrated application of these two quality assurance activities within a unified framework for JIT-SQA throughout the software development process. We conducted experiments using both statically typed (Java) and dynamically typed (Python) languages by integrating various testing techniques. The results indicate that, after implementing the novel update and dynamic selection mechanisms, our framework significantly outperforms simple integrated frameworks of defect prediction and testing. Specifically, the Recall improves by 21.6%, 10.5%, and 6.3% compared with the testing-integrated versions of ODaSC, ECo-HumLa, and HumLa, respectively, while the F1 score improves by 13.2%, 7.0%, and 5.1%. At the same time, in terms of guiding testing activities, the total number of tests is reduced by 2.9%, 19.5%, and 24.8% compared with the same baselines. These findings demonstrate that our framework not only mitigates the adverse effects of concept drift, verification latency, and class imbalance in code evolution scenarios but also effectively guides testing to reduce resource consumption.

Software Quality Assurance (SQA) is essential in software development and many defect prediction methods based on machine learning have been proposed to identify defective modules. However, most existing defect prediction models do not provide good defect prediction results, and the semantic features reflecting the detective patterns may not be well-captured via traditional feature extraction methods. More information such as code comments should be also be embedded to generate semantic features respecting the source code functionality. Therefore, how to embed code comments for defect prediction is a big challenge, and another problem is that many comments of source code are missing in real-world applications. In this paper, we propose a novel defect prediction model named CAP-CNN (Convolutional Neural Network for Comments Augmented Programs), which is a deep learning model that automatically embeds code comments in generating semantic features from the source code for software defect prediction. To overcome the missing comments problem, a novel training strategy is used in CAP-CNN that the network encodes and absorb comments information to generate semantic features automatically during training process, which does not need testing modules to contain comments. Experimental results on several widely-used software data sets indicate that the comment features are able to improve defect prediction performance.

Defect prediction is an active topic in software quality assurance, which can help developers find potential bugs and make better use of resources. To improve prediction performance, this paper introduces cross-entropy, one common measure for natural language, as a new code metric into defect prediction tasks and proposes a framework called DefectLearner for this process. We first build a recurrent neural network language model to learn regularities in source code from software repository. Based on the trained model, the cross-entropy of each component can be calculated. To evaluate the discrimination for defect-proneness, cross-entropy is compared with 20 widely used metrics on 12 open-source projects. The experimental results show that cross-entropy metric is more discriminative than 50% of the traditional metrics. Besides, we combine cross-entropy with traditional metric suites together for accurate defect prediction. With cross-entropy added, the performance of prediction models is improved by an average of 2.8% in F1-score.

Isolated ventricular septal defects: An anatomic-hemodynamic correlation∗

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.

An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve software quality and expedite testing procedures, hence redefining defect prevention for American businesses. This study uses a systematic methodology that combines machine learning-based defect prediction with real-world case studies, analyzing defect trends and evaluating the effectiveness of predictive models. The results show that enterprises leveraging predictive analytics in QA automation experience higher defect detection rates reduced testing overhead, and faster release cycles. The study identifies key machine learning models, such as Random Forests, Support Vector Machines (SVM), and Neural Networks, which have demonstrated significant accuracy in defect prediction. It also discusses the integration of predictive analytics within DevOps and CI/CD pipelines, enabling continuous monitoring and proactive defect prevention. Defect prediction skills will be significantly improved in the future by developments in Explainable AI (XAI), deep learning models, and Natural Language Processing (NLP). In addition to supporting data-driven decision-making, model transparency, and continuous learning frameworks, this article offers important advice for businesses looking to integrate predictive analytics into their QA procedures. U.S. businesses may go from reactive to proactive QA approaches by adopting predictive analytics, which will guarantee better software quality, lower expenses, and an enhanced user experience

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.

Defect prediction models can help Software Quality Assurance (SQA) teams understand their past pitfalls that lead to defective modules. However, the conclusions that are derived from defect prediction models without mitigating redundant metrics issues may be misleading. In this paper, we set out to investigate if redundant metrics issues are affecting defect prediction studies, and its degree and causes of redundancy. Through a case study of 101 publicly-available defect datasets of systems that span both proprietary and open source domains, we observe that (1) 10%-67% of metrics of the studied defect datasets are redundant, and (2) the redundancy of metrics has to do with the aggregation functions of metrics. These findings suggest that researchers should be aware of redundant metrics prior to constructing a defect prediction model in order to maximize internal validity of their studies.

Background & Problem Statement - Software testing is a critical phase in the software development lifecycle (SDLC), ensuring that applications function correctly, meet user requirements, and maintain high- quality standards. Traditional software testing approaches, including manual testing and rule-based automation, often face challenges in scalability, efficiency, and adaptability to dynamic software environments. Traditional testing methods are overwhelmed by complex software systems which slows down defect detection and extends both testing costs and release schedules. Machine Learning (ML) has emerged as a transformative solution, introducing predictive and adaptive capabilities that optimize test case selection, automate defect detection, and enhance overall software quality assurance (QA). This study explores the integration of ML in software testing, addressing the challenges of traditional QA methodologies and demonstrating how AI-driven frameworks improve testing efficiency. Methodology - To investigate the impact of ML in software testing, this research adopts a systematic approach by analyzing ML-driven test automation techniques, including predictive testing, adaptive test execution, and automated test case generation. Research reviews how Google Microsoft Facebook IBM and Deep Code put ML-based quality assurance frameworks into operation. The study leverages supervised learning, reinforcement learning, deep learning, and NLP-based techniques to demonstrate how ML models predict software defects, dynamically adapt test cases, and optimize testing resources. The research tests how ML-based testing models operate within CI/CD pipelines to improve ongoing testing and deployment flow. Analysis & Results - The analysis of ML-driven software testing reveals that predictive analytics improves early defect detection rates. It helps developers spend 37% less time debugging their work. Adaptive testing models, including self-healing test scripts, minimize maintenance costs by 50% and enhance test reliability in agile environments. The integration of NLP-based test case generation increases test coverage. NLP technology enables automatic connection between requirements and test cases at 89% success rate. Additionally, reinforcement learning techniques improve test case selection, reducing redundant test executions by 43%. Our research shows different ML methods work well to lessen incorrect error alerts. ML integration for QA surely increasing defect prediction accuracy and optimizing test execution time. Findings & Contributions - This research contributes to the field of AI-driven software testing by providing a comprehensive framework for ML-based QA methodologies. Our study shows that machine learning helps find more software problems better adapts test cases and lowers testing expenses to solve present software development needs. The study also identifies critical challenges, including data availability, model interpretability, and computational overhead, suggesting future research directions in Explainable AI (XAI), hybrid AI-ML testing models, and AI-driven security testing. As the industry moves toward AI-first software testing, this research paves the way for fully autonomous QA frameworks, enabling intelligent, scalable, and cost- effective software validation techniques. Keywords - Machine Learning, Software Testing, Quality Assurance, Predictive Testing, Adaptive Testing, Test Automation, Defect Prediction, Self- Healing Test Scripts, AI-Driven QA, Reinforcement

Severe software defects can lead to significant issues and even result in substantial financial losses. As a result, automated code defect detection has garnered widespread attention. To fix these defects as soon as possible, J ust- I n- T ime D efect P rediction (JIT-DP) and J ust- I n- T ime D efect L ocalization (JIT-DL) have been investigated. Specifically, JIT-DP aims to predict commit-level defects when a code change is submitted, and JIT-DL aims to perform line-level defect localization before they lead to failures. Several methods have been developed for JIT-DP and JIT-DL, with those using C ode P re- T rained M odels (CPTMs) leading to the best results. However, most previous studies concentrate on JIT-DP, even though JIT-DL is arguably more crucial. Identifying which specific lines of a commit are defective facilitates the prediction of whether the commit is defective. Therefore, we propose a M ulti- T ask L earning method (JIT-MTL) designed to address both JIT-DP and JIT-DL. Specifically, we train a CPTM to simultaneously identify defective commits and lines. The line-level predictions are directly used for JIT-DL, while the commit-level predictions for the same commit are merged to determine the results for JIT-DP. To enhance the accuracy of line-level predictions, we provide the CPTM with additional commit-level information, including commit-level defect code representation and expert features. Experimental results on the JIT-Defect4J dataset show that JIT-MTL outperforms the state-of-the-art baselines, with improvements of 14% in F1 in JIT-DP, 53.9% in Top-10 Accuracy, and 36.4% in Top-5 Accuracy in JIT-DL.

BACKGROUND: Correction and prevention of severe cicatricial deformities is a labor-intensive task for doctors of aesthetic and rehabilitation medicine. Often, as a result of skin scarring, the psychological status of the patient changes due to the presence of persistent subjective sensations or the location of defects in open, functionally active areas of the skin. The steady annual growth in trauma, the number of surgical interventions and invasive procedures actualizes the question of finding an optimal, effective and widely available method for preventing severe skin scarring or correcting fibrous deformities.
 AIM: scientific and practical substantiation of the use of enzyme gel ultraphonophoresis with direct proteolytic activity in patients with hypertrophic skin scars.
 MATERIALS AND METHODS: A prospective interventional cohort comparative study of the efficacy and safety of enzyme gel ultraphonophoresis with direct proteolytic activity in patients with hypertrophic skin scars was performed. The study included 42 patients with a diagnosis of "Hypertrophic scars" (ICD-10, L91) of postoperative or post-traumatic genesis, divided into observation and comparison groups with emerging (n=20) and mature (n=22) scars. In the course of the study, clinical, instrumental and medical and sociological research methods were used.
 RESULTS: A pronounced positive dynamic of indicators of clinical and instrumental parameters of scar tissue was established, improvement of quality-of-life indicators was the most significant in patients of the observation group with developing scars. The expediency of using the enzyme gel ultraphonophoresis technique with direct proteolytic activity in patients with hypertrophic skin scars is confirmed by a combination of parameters: positive dynamics of the clinical status, functional properties of scar tissue and quality of life indicators, a high safety profile and patient satisfaction with treatment. The effectiveness of correction in patients with developing and mature hypertrophic scars was 83–87%.
 CONCLUSION: The therapeutic and preventive technology of enzyme gel ultraphonophoresis with direct proteolytic activity is an effective and promising method for the correction and prevention of postoperative and post-traumatic cicatricial skin defects.

Software fault prediction plays a crucial role in enhancing software quality and reliability by enabling early detection of defect-prone modules. This study proposes a machine learning-based predictive framework to identify software faults using historical software metrics and defect data. The objective is to evaluate and compare the performance of various machine learning algorithms—Random Forest, XGBoost, Support Vector Machine (SVM), and Neural Networks—for their effectiveness in fault detection. Public benchmark datasets comprising object-oriented metrics such as CK, Halstead, and McCabe were used after undergoing pre-processing steps including normalization; class balancing using SMOTE, and feature selection through PCA and RFE. Models were trained and tested using stratified k-fold cross-validation, and evaluated using metrics such as Accuracy, Precision, Recall, F1-score, and ROC-AUC. Among the models, XGBoost outperformed others, achieving the highest F1-score and ROC-AUC, followed by Random Forest. Feature importance analysis and SHAP value interpretation provided insights into key predictors contributing to software faults, enhancing the model's transparency and practical usability. The study demonstrates that ensemble learning methods significantly improve the fault prediction process, making them suitable for deployment in real-world software quality assurance workflows. These findings contribute to the ongoing advancement of predictive software engineering and open new avenues for integrating intelligent fault detection systems in continuous integration and DevOps environments.

The Zero Defect Manufacturing (ZDM) concept combined with Artificial Intelligence (AI), a key enabling technology, opens up new opportunities for improved quality management and advanced problem-solving. However, there is a lack of applied research in industrial plants that would allow for the widespread deployment of this framework. Thus, the purpose of this article was to apply AI in an industrial application in order to develop application insights and identify the necessary prerequisites for achieving ZDM. A case study was done at a Swedish manufacturing plant to evaluate the implementation of a defect-detection strategy on products prone to misclassification and on an imbalanced data set with very few defects. A semi-supervised learning approach was used to learn which vibration properties differentiate confirmed defects from approved products. This method enabled the calculation of a defect similarity ratio that was used to predict how similar newly manufactured products are to defective products. This study identified four prerequisites and four insights critical for the development of an AI solution supporting ZDM. The key finding demonstrates how well traditional and innovative quality methods complement one another. The results highlight the importance of starting data science projects quickly to ensure data quality and allow a ZDM detection strategy to build knowledge to allow for the development of more proactive strategies, such as the prediction and prevention of defects.

Recent research found that fine‐tuning pre‐trained models is superior to training models from scratch in just‐in‐time (JIT) defect prediction. However, existing approaches using pre‐trained models have their limitations. First, the input length is constrained by the pre‐trained models.Secondly, the inputs are change‐agnostic.To address these limitations, we propose JIT‐Block, a JIT defect prediction method that combines multiple input semantics using changed block as the fundamental unit. We restructure the JIT‐Defects4J dataset used in previous research. We then conducted a comprehensive comparison using eleven performance metrics, including both effort‐aware and effort‐agnostic measures, against six state‐of‐the‐art baseline models. The results demonstrate that on the JIT defect prediction task, our approach outperforms the baseline models in all six metrics, showing improvements ranging from 1.5% to 800% in effort‐agnostic metrics and 0.3% to 57% in effort‐aware metrics. For the JIT defect code line localization task, our approach outperforms the baseline models in three out of five metrics, showing improvements of 11% to 140%.

In this work we propose a machine-learning MO based on Naive-Bayes classification (NB-MO) for the diagnostic tasks of detection, localization and assessment of perfusion defects in clinical SPECT Myocardial Perfusion Imaging (MPI), with the goal of evaluating several image reconstruction methods used in clinical practice. NB-MO uses image features extracted from polar-maps in order to predict lesion detection, localization and severity scores given by human readers in a series of 3D SPECT-MPI. The population used to tune (i.e. train) the NB-MO consisted of simulated SPECT-MPI cases – divided into normals or with lesions in variable sizes and locations – reconstructed using filtered backprojection (FBP) method. An ensemble of five human specialists (physicians) read a subset of simulated reconstructed images, and assigned a perfusion score for each region of the left-ventricle (LV). Polar-maps generated from the simulated volumes along with their corresponding human scores were used to train five NB-MOs (one per human reader), which are subsequently applied (i.e. tested) on three sets of clinical SPECT-MPI polar maps, in order to predict human detection and localization scores. The clinical “testing” population comprises healthy individuals and patients suffering from coronary artery disease (CAD) in three possible regions, namely: LAD, LcX and RCA. Each clinical case was reconstructed using three reconstruction strategies, namely: FBP with no SC (i.e. scatter compensation), OSEM with Triple Energy Window (TEW) SC method, and OSEM with Effective Source Scatter Estimation (ESSE) SC. Alternative Free-Response (AFROC) analysis of perfusion scores shows that NB-MO predicts a higher human performance for scatter-compensated reconstructions, in agreement with what has been reported in published literature. These results suggest that NB-MO has good potential to generalize well to reconstruction methods not used during training, even for reasonably dissimilar datasets (i.e. simulated vs. clinical).