Purpose: Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.Design/methodology/approach: The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.Findings: The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.Originality/value: Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

The development of reliable software depends heavily on the effective collaboration between teams responsible for development and testing. Despite ongoing efforts, many software programs still contain bugs that can lead to financial losses and business risks. Therefore, detecting and fixing software defects after release is crucial. While binary classification methods have been commonly used for this purpose, recent Artificial Intelligence (AI) advancements offer new opportunities for software teams to create more robust software. To address challenges in Software Defect Prediction (SDP), we conducted a thorough bibliographic survey of 79 research articles from the year 2011 to 2023 that examined previous models, datasets, data validation techniques, defect detection, prediction methods, and SDP tools. The survey revealed that previous research often lacked appropriate datasets with the necessary characteristics and data validation methods. Additionally, many standard datasets suffer from a lack of labels, which hinders effective defect detection. Systematic literature reviews on SDP are scarce, further emphasizing the importance of this study. Based on the findings, we provide crucial recommendations for designing effective SDP models and tools. The proposed survey outlines an architecture for constructing SDP datasets with the appropriate characteristics, as well as multi-label classification and data validation methodologies for software defects. This approach aims to enhance SDP research and contribute to the development of high-quality software products by improving defect prediction accuracy.

Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository’s datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model’s trustworthiness in the SDLC.

Heterocyclic compounds constitute the most important part of medicinal as well as organic chemistry. Most of the marketed drugs possess therapeutic activity because of the presence of heterocyclic scaffolds as part of their structure. A slight change in the structure of the heterocyclic moieties may result in a major change in the therapeutic response of the drug candidate. Among all heterocycle compounds, the compounds containing nitrogen and sulfur atoms serve as a unique resource for drug development, such as benzothiazoles. Benzothiazole is a benzofused heterocyclic that is widely reported as a constituent of naturally occurring chemicals and chiefly responsible for their pharmacological potential. It was also reported that the pharmacological activity of BTA may also be influenced by its coupling with aldehydes, ketones, or hydrazines to form respected benzothiazole-hydrazone derivatives. The present comprehensive review consists of various synthesis methods, biological activities, and structure-activity relationships of and targets of benzothiazole and benzothiazole-hydrazone derivatives to provide a wide range of information to medicinal chemists for future research work.