Software defect prediction (SDP) in the initial period of the software development life cycle (SDLC) remains a critical and important assignment. SDP is essentially studied during few last decades as it leads to assure the quality of software systems. The quick forecast of defective or imperfect artifacts in software development may serve the development team to use the existing assets competently and more effectively to provide extraordinary software products in the given or narrow time. Previously, several canvassers have industrialized models for defect prediction utilizing machine learning (ML) and statistical techniques. ML methods are considered as an operative and operational approach to pinpoint the defective modules, in which moving parts through mining concealed patterns amid software metrics (attributes). ML techniques are also utilized by several researchers on healthcare datasets. This study utilizes different ML techniques software defect prediction using seven broadly used datasets. The ML techniques include the multilayer perceptron (MLP), support vector machine (SVM), decision tree (J48), radial basis function (RBF), random forest (RF), hidden Markov model (HMM), credal decision tree (CDT), K-nearest neighbor (KNN), average one dependency estimator (A1DE), and Naïve Bayes (NB). The performance of each technique is evaluated using different measures, for instance, relative absolute error (RAE), mean absolute error (MAE), root mean squared error (RMSE), root relative squared error (RRSE), recall, and accuracy. The inclusive outcome shows the best performance of RF with 88.32 average accuracy and 2.96 rank value, second-best performance is achieved by SVM with 87.99 average accuracy and 3.83 rank values. Moreover, CDT also shows 87.88 average accuracy and 3.62 rank values, placed on the third position. The comprehensive outcomes of research can be utilized as a reference point for new research in the SDP domain, and therefore, any assertion concerning the enhancement in prediction over any new technique or model can be benchmarked and proved.

The revolution of the Internet of Things (IoT) is transforming many concepts, making them “Smart”. It has revolutionized many areas of real life. Smart City is one of the key concepts of this revolution. Although cities are being digitally transformed, it still has obstacles along the way. In this paper, we have analyzed different publications for our Systematic Literature Review (SLR). This study highlights the areas where the blockchain is used and determines the benefits of using blockchain. The main contribution is to explore and identify the barriers and hurdles in Smart City Domain and how these hurdles are mitigated by the blockchain technology. This Systematic Study also addresses different challenges and issues such as security, immutability, interoperability, decentralization, privacy, and trust in the development of Smart Cities. An overview of the systematic research is also presented that would help identify the most and least studied concerns addressed in this study. This paper aims at analyzing how blockchain technology is used in different smart city business models, what are the features of blockchain that could consider it to be used beyond crypto currencies. We hope that this study can inspire interest in theory and exercise to stand-in discussions in this area adhere to these limits.

Analogy-Based Estimation (ABE) is one of the promising estimation models used for predicting the software development effort. Researchers proposed different variants of the ABE model, but still, the most suitable procedure could not be produced for accurate estimation. In this study, an artificial Bee colony guided Analogy-Based Estimation (BABE) model is proposed which ensembles Artificial Bee Colony (ABC) with ABE for accurate estimation. ABC produces different weights, out of which the most appropriate is infused in the similarity function of ABE during the stage of model training, which are later used in the testing stage for evaluation. There are six real datasets utilized for simulating the model procedure. Five of these datasets are taken from the PROMISE repository. The predictive performance is improved for BABE over the existing ones. The most significant of its performance is found on the International Software Benchmarking Standards Group (ISBSG) dataset.

Nonfunctional requirements are ignored because functional requirements are considered to be more important than nonfunctional requirements, lack of knowledge and methodologies to accomplish nonfunctional requirement elicitation and the nature of agile software development process. This ignorance of nonfunctional requirements become the cause to failure of projects. Furthermore, cloud computing helps to practice twelve (12) agile principles including nonfunctional requirement elicitation. This study proposed a semi-automated methodology for eliciting nonfunctional requirement in agile development and cloud computing environment. The methodology used an NLP based automatic NFR extraction approach to fast the NFR elicitation process. The methodology is evaluated by applying on eProcurement dataset. The results are improved as compared to existing studies.

Software testing is an important and cost intensive activity in software development. The major contribution in cost is due to test case generations. Requirement-based testing is an approach in which test cases are derivative from requirements without considering the implementation’s internal structure. Requirement-based testing includes functional and nonfunctional requirements. The objective of this study is to explore the approaches that generate test cases from requirements. A systematic literature review based on two research questions and extensive quality assessment criteria includes studies. The study identifies 30 primary studies from 410 studies spanned from 2000 to 2018. The review’s finding shows that 53% of journal papers, 42% of conference papers, and 5% of book chapters’ address requirements-based testing. Most of the studies use UML, activity, and use case diagrams for test case generation from requirements. One of the significant lessons learned is that most software testing errors are traced back to errors in natural language requirements. A substantial amount of work focuses on UML diagrams for test case generations, which cannot capture all the system’s developed attributes. Furthermore, there is a lack of UML-based models that can generate test cases from natural language requirements by refining them in context. Coverage criteria indicate how efficiently the testing has been performed 12.37% of studies use requirements coverage, 20% of studies cover path coverage, and 17% study basic coverage.

Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) achieve the best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew’s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB, and CDT achieve better results.