Search

Software defect identification (SDI) is a key part of improving the quality of software projects and lowering the risks that along with maintenance. It does identify the software defect causes that have not been reached yet to get sufficient results. On the other hand, many researchers have recently developed several models, including NN, ML, DL, advanced CNN, and LSTM, to enhance the effectiveness of defect prediction. Due to an insufficient dataset size, repeated investigations, and no longer appropriate baseline selection, the research on the CNN model was unable to produce reliable results. In addition, XAI a well-known explainability approach creates deep models in computer vision, as well as successfully handles the software defect prediction that is easy for humans to understand. To address these issues, firstly we have used SMOTE for preprocessing which was collected from the NASA repository; categorical and numerical data. Secondly, we have experimented with software defect prediction using 1D-CNN and 2D-CNN named lightweight CNN (LCNN). Subsequently, evaluation we have employed a 100-repetition holdout validation. For the cross-validation setup, we utilized the 1D-CNN model was $20\times 1$ , and for the 2D-CNN model, it was $4\times 5 \times 1$ . After that, the results of the experiment were compared and assessed in terms of accuracy, MSE, and AUC. The result shows that 2D-CNN shows 1.36% better contrast with 1D-CNN. Thirdly, we have conducted research on the identification of software defect features via LIME and SHAP in XAI stand as state-of-the-art techniques. However, we cannot use 2D-CNN because it involves more complex relationships, making it challenging to create transparent explanations. That is why we have realized that 1D-CNN will superior result to explain the root cause of software feature identifications. Finally, LIME provides accurate visualization of software defect features in contrast with SHAP, as well as it helps the stakeholders of the software industry easily find actual root causes of software defect identification.

The rising deployment of software in automation and the cognitive skills of machines indicate a machine revolution in modern human civilization. Thus, diagnosing and predicting software faults is crucial to software reliability. In this paper, we first preprocessed four real datasets offered by National Aeronautics and Space Administration with twenty-one features using the Synthetic Minority Oversampling Technique and Label Encoding techniques. Subsequently, we experimented with thirteen software fault diagnosis Machine Learning (ML) models, i.e., Random Forest Regression, Linear Regression, Naïve Bayes, Decision Tree Classifier, Logistic Regression, KNeighbors Classifier, AdaBoost, Gradient Boosting Classifier, Gradient Boosting Regression, XGBR Regressor, XGBoost Classifier, Extra Trees Classifier and Support Vectors Machine after that, we compared each ML Model to select the best diagnostic model. Among them, XGBR outperformed, considering the accuracy, mean square error, and R2 score. We also used Explainable Artificial Intelligence (XAI), Local Interpretable Model (LIME), and SHapley Additive exPlanations (SHAP) to determine software fault features. We observed that Number of static invocations (nosi), Depth Inheritance Tree (dit), and Coupling Between Objects (cbo) features are the most affected software faults feature from datasets. For LIME, the average True positive of nosi is 40%, dit is 15%, and cbo is 20%; on the other hand, the SHAP average true positive value of nosi is 36%, cbo is 15%, and the norm true negative value of dit is 5%. Thus, LIME can afford the greatest impact on the model outcomes to identify features that are the most significant reasons for software defects.

A community level field survey is conducted in the coastal zone of Bangladesh to assess the effectiveness of five selected planned adaptations against storm surge in micro scale which are already proved effective in macro scale. The five selected planned adaptations are: cyclone shelter, plantation, paka & semi-paka house, loan, and communication infrastructure. The selected study area is well-known for storm surge hazard. The study site and sample size are selected by using Probability Proportional to Size (PPS) and cluster sampling methods. Semi-structured household questionnaire, Focus Group Discussion (FGD), and Key Informative Interviews (KII) were conducted for understanding community perceptions on adaptations. Effectiveness of planned adaptations are assessed by applying an Index developed by using five-point Likert scale. Barriers against the adaptations are assessed by probabilistic analysis and represented by Venn diagram. Interrelationships among the adaptations are assessed through causal relation network. The study results show a mismatch of effectiveness of planned adaptations between the macro scale and micro scale. It is found that cyclone shelter, paka & semi-paka house, loan, and communication infrastructure do not exhibit a similar level of effectiveness in micro scale as these are expected to be effective in macro scale. Among the five selected planned adaptations, plantation is the only adaption which is found to be effective in both micro scale and macro scale because of its high economic return for the community. The reasons for the mismatch of this effectiveness between macro scale and micro scale are – barriers and causal relation for these adaptations in micro scale are not considered in macro scale. A comprehensive analysis is recommended to ensure a similar level of effectiveness of an adaptation both in macro scale and in micro scale.