CodeT5+: CodeT5+ was initially chosen as one of the baselines because it was among the top-performing models in our experiments on defect prediction (Monteiro et al., 2025). Although CodeT5+ does not contain an explicit [CLS] token, as in BERT-based language models, we still use the first encoded token as the head of the classification layer. Therefore, we maintain the default practice of inspecting the weights of the first token attention heads. UniXCoder: In the same way as in CodeT5+, UniXCoder was also among the top performers in defect prediction experiments (Monteiro et al., 2025), so we keep the same default strategy of using the first encoded token attention weights. We also initially considered JIT-Block (Huang et al., 2024) and JIT-CF (Ju et al., 2025). Regarding JIT-Block, its authors reconstructed the dataset (JIT-Defects4J) into the changed block format, which preserves the relative positional information between added and deleted code lines -- information lost in traditional datasets -- thus facilitating the model's ability to learn the semantic meaning of code changes. So, as the dataset was changed, it would not be possible to conduct a fair comparison. Finally, according to its published results, JIT-CF does not achieve better results than JIT-Smart. A consolidated overview of the baseline classifiers is presented in Table 2. 3.4 Description of the Experiments RQ1 How do pre-trained language models perform in comparison to traditional machine learning approaches for continual within-project and cross-project Just-in-Time Software Defect Prediction (JIT-SDP)?

An AI-ML-powered quality engineering approach uses AI-ML to enhance software quality assessments by predicting defects. Existing ML models struggle with noisy data types, imbalances, pattern recognition, feature extraction, and generalization. To address these challenges, we develop a new model, Adaptive Differential Evolution (ADE) based Quantum Variational Autoencoder-Transformer (QVAET) Model (ADE-QVAET). ADE combines with QVAET to obtain high-dimensional latent features and maintain sequential dependencies, resulting in enhanced defect prediction accuracy. ADE optimization enhances model convergence and predictive performance. ADE-QVAET integrates AI-ML techniques such as tuning hyperparameters for scalable and accurate software defect prediction, representing an AI-ML-driven technology for quality engineering. During training with a 90% training percentage, ADE-QVAET achieves high accuracy, precision, recall, and F1-score of 98.08%, 92.45%, 94.67%, and 98.12%, respectively, when compared to the Differential Evolution (DE) ML model.

Just-in-Time software defect prediction (JIT-SDP) plays a critical role in prioritizing risky code changes during code review and continuous integration. However, existing datasets often suffer from noisy labels and low precision in identifying bug-inducing commits. To address this, we present ReDef (Revert-based Defect dataset), a high-confidence benchmark of function-level modifications curated from 22 large-scale C/C++ projects. Defective cases are anchored by revert commits, while clean cases are validated through post-hoc history checks. Ambiguous instances are conservatively filtered out via a GPT-assisted triage process involving multiple votes and audits. This pipeline yields 3,164 defective and 10,268 clean modifications, offering substantially more reliable labels than prior existing resources. Beyond dataset construction, we provide the first systematic evaluation of how pre-trained language models (PLMs) reason about code modifications -- specifically, which input encodings most effectively expose change information, and whether models genuinely capture edit semantics. We fine-tune CodeBERT, CodeT5+, and UniXcoder under five encoding strategies, and further probe their sensitivity through counterfactual perturbations that swap added/deleted blocks, invert diff polarity, or inject spurious markers. Our results show that compact diff-style encodings consistently outperform whole-function formats across all PLMs, with statistical tests confirming large, model-independent effects. However, under counterfactual tests, performance degrades little or not at all -- revealing that what appears to be robustness in fact reflects reliance on superficial cues rather than true semantic understanding. These findings indicate that, unlike in snapshot-based tasks, current PLMs remain limited in their ability to genuinely comprehend code modifications.

An AI-powered quality engineering platform uses artificial intelligence to boost software quality assessments through automated defect prediction and optimized performance alongside improved feature extraction. Existing models result in difficulties addressing noisy data types together with imbalances, pattern recognition complexities, ineffective feature extraction, and generalization weaknesses. To overcome those existing challenges in this research, we develop a new model Adaptive Differential Evolution based Quantum Variational Autoencoder-Transformer Model (ADE-QVAET), that combines a Quantum Variational Autoencoder-Transformer (QVAET) to obtain high-dimensional latent features and maintain sequential dependencies together with contextual relationships, resulting in superior defect prediction accuracy. Adaptive Differential Evolution (ADE) Optimization utilizes an adaptive parameter tuning method that enhances model convergence and predictive performance. ADE-QVAET integrates advanced AI techniques to create a robust solution for scalable and accurate software defect prediction that represents a top-level AI-driven technology for quality engineering applications. The proposed ADE-QVAET model attains high accuracy, precision, recall, and f1-score during the training percentage (TP) 90 of 98.08%, 92.45%, 94.67%, and 98.12%.

In recent years, the rise of autonomous driving technologies has highlighted the critical importance of reliable software for ensuring safety and performance. This paper proposes a novel approach for just-in-time software defect prediction (JIT-SDP) in autonomous driving software systems using multimodal learning. The proposed model leverages the multimodal transformers in which the pre-trained transformers and a combining module deal with the multiple data modalities of the software system datasets such as code features, change metrics, and contextual information. The key point for adapting multimodal learning is to utilize the attention mechanism between the different data modalities such as text, numerical, and categorical. In the combining module, the output of a transformer model on text data and tabular features containing categorical and numerical data are combined to produce the predictions using the fully connected layers. Experiments conducted on three open-source autonomous driving system software projects collected from the GitHub repository (Apollo, Carla, and Donkeycar) demonstrate that the proposed approach significantly outperforms state-of-the-art deep learning and machine learning models regarding evaluation metrics. Our findings highlight the potential of multimodal learning to enhance the reliability and safety of autonomous driving software through improved defect prediction.

Software defect prediction is a critical aspect of software quality assurance, as it enables early identification and mitigation of defects, thereby reducing the cost and impact of software failures. Over the past few years, quantum computing has risen as an exciting technology capable of transforming multiple domains; Quantum Machine Learning (QML) is one of them. QML algorithms harness the power of quantum computing to solve complex problems with better efficiency and effectiveness than their classical counterparts. However, research into its application in software engineering to predict software defects still needs to be explored. In this study, we worked to fill the research gap by comparing the performance of three QML and five classical machine learning (CML) algorithms on the 20 software defect datasets. Our investigation reports the comparative scenarios of QML vs. CML algorithms and identifies the better-performing and consistent algorithms to predict software defects. We also highlight the challenges and future directions of employing QML algorithms in real software defect datasets based on the experience we faced while performing this investigation. The findings of this study can help practitioners and researchers further progress in this research domain by making software systems reliable and bug-free.

Predicting software defects early in the development process not only enhances the quality and reliability of the software but also decreases the cost of development. A wide range of machine learning techniques can be employed to create software defect prediction models, but the effectiveness and accuracy of these models are often influenced by the choice of appropriate feature subset. Since finding the optimal feature subset is computationally intensive, heuristic and metaheuristic approaches are commonly employed to identify near-optimal solutions within a reasonable time frame. Recently, the quantum computing paradigm quantum annealing (QA) has been deployed to find solutions to complex optimization problems. This opens up the possibility of addressing the feature subset selection problem with a QA machine. Although several strategies have been proposed for feature subset selection using a QA machine, little exploration has been done regarding the viability of a QA machine for feature subset selection in software defect prediction. This study investigates the potential of D-Wave QA system for this task, where we formulate a mutual information (MI)-based filter approach as an optimization problem and utilize a D-Wave Quantum Processing Unit (QPU) solver as a QA solver for feature subset selection. We evaluate the performance of this approach using multiple software defect datasets from the AEEM, JIRA, and NASA projects. We also utilize a D-Wave classical solver for comparative analysis. Our experimental results demonstrate that QA-based feature subset selection can enhance software defect prediction. Although the D-Wave QPU solver exhibits competitive prediction performance with the classical solver in software defect prediction, it significantly reduces the time required to identify the best feature subset compared to its classical counterpart.

Is there a statistical difference between Naive Bayes and Random Forest in terms of recall, f-measure, and precision for predicting software defects? By utilizing systematic literature review and meta-analysis, we are answering this question. We conducted a systematic literature review by establishing criteria to search and choose papers, resulting in five studies. After that, using the meta-data and forest-plots of five chosen papers, we conducted a meta-analysis to compare the two models. The results have shown that there is no significant statistical evidence that Naive Bayes perform differently from Random Forest in terms of recall, f-measure, and precision.

Before researchers rush to reason across all available data, they should first check if the information is densest within some small region. We say this since, in 240 GitHub projects, we find that the information in that data ``clumps'' towards the earliest parts of the project. In fact, a defect prediction model learned from just the first 150 commits works as well, or better than state-of-the-art alternatives. Using just this early life cycle data, we can build models very quickly (using weeks, not months, of CPU time). Also, we can find simple models (with just two features) that generalize to hundreds of software projects. Based on this experience, we warn that prior work on generalizing software engineering defect prediction models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data now needs to be revisited since their conclusions were drawn from relatively uninformative regions. Replication note: all our data and scripts are online at https://github.com/snaraya7/early-defect-prediction-tse.

The increased computerization in recent years has resulted in the production of a variety of different software, however measures need to be taken to ensure that the produced software isn't defective. Many researchers have worked in this area and have developed different Machine Learning-based approaches that predict whether the software is defective or not. This issue can't be resolved simply by using different conventional classifiers because the dataset is highly imbalanced i.e the number of defective samples detected is extremely less as compared to the number of non-defective samples. Therefore, to address this issue, certain sophisticated methods are required. The different methods developed by the researchers can be broadly classified into Resampling based methods, Cost-sensitive learning-based methods, and Ensemble Learning. Among these methods. This report analyses the performance of the Online Sequential Extreme Learning Machine (OS-ELM) proposed by Liang et.al. against several classifiers such as Logistic Regression, Support Vector Machine, Random Forest, and Na\"ive Bayes after oversampling the data. OS-ELM trains faster than conventional deep neural networks and it always converges to the globally optimal solution. A comparison is performed on the original dataset as well as the over-sampled data set. The oversampling technique used is Cluster-based Over-Sampling with Noise Filtering. This technique is better than several state-of-the-art techniques for oversampling. The analysis is carried out on 3 projects KC1, PC4 and PC3 carried out by the NASA group. The metrics used for measurement are recall and balanced accuracy. The results are higher for OS-ELM as compared to other classifiers in both scenarios.