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This paper is focused at studying the current state of research involving the four dimensions of defect management strategy, i.e. software defect analysis, software quality, software reliability and software development cost/effort.

The methodology developed by Kitchenham (2007) is followed in planning, conducting and reporting of the systematic review. Out of 625 research papers, nearly 100 primary studies related to our research domain are considered. The study attempted to find the various techniques, metrics, data sets and performance validation measures used by researchers.

The study revealed the need for integrating the four dimensions of defect management and studying its effect on software performance. This integrated approach can lead to optimal use of resources in software development process.

There are many dimensions in defect management studies. The authors have considered only vital few based on the practical experiences of software engineers. Most of the research work cited in this review used public data repositories to validate their methodology and there is a need to apply these research methods on real datasets from industry to realize the actual potential of these techniques.

The authors believe that this paper provides a comprehensive insight into the various aspects of state-of-the-art research in software defect management. The authors feel that this is the only research article that delves into the four facets namely software defect analysis, software quality, software reliability and software development cost/effort.

The aim of this paper is to highlight the application of six sigma, software engineering techniques and simulation to software development with a view to improving the software process and product.

This paper attempts to integrate six sigma and simulation to define, analyse, measure and predict various elements of software development (such as cost, schedule, defects) that influence software quality, thereby helping the software personnel take necessary measures early in the development process to improve the software processes and remove defects. Simulation results provide qualitative and quantitative suggestions on the ways to change the software process to achieve six sigma quality. The integration of six sigma and CMM and the role of knowledge management in software organisations have been taken into account.

Most software organisations operate between 2.3 and 3 sigma level. This paper presents a framework for definition, measurement, and analysis of important elements of the software product and process using six sigma tools and exploits the use of simulation in bringing six sigma improvements. Case studies have been presented to demonstrate the findings.

Application of the techniques presented in this paper would definitely improve software organisations' processes and product.

The adoption of methodologies outlined in this paper in software companies would enable them to attain improvements in terms of cost, schedule and quality.

The integration of simulation with six sigma applied to software development is novel in this paper. This paper will be valuable for quality professionals and management personnel in software organisations.

Managing project completion within the stipulated time is significant to all firms' sustainability. Especially for software start-up firms, it is of utmost importance. For any schedule variation, these firms must spend 25 to 40 percent of the development cost reworking quality defects. Significantly, the existing literature does not support defect rework opportunities under quality aspects among Indian IT start-ups. The present study aims to fill this niche by proposing a unique mathematical model of the defect rework aligned with the Six Sigma quality approach.

An optimization model was formulated, comprising the two objectives: rework “time” and rework “cost.” A case study was developed in relevance, and for the model solution, we used MATLAB and an elitist, Nondominated Sorting Genetic Algorithm (NSGA-II).

The output of the proposed approach reduced the “time” by 31 percent at a minimum “cost”. The derived “Pareto Optimal” front can be used to estimate the “cost” for a pre-determined rework “time” and vice versa, thus adding value to the existing literature.

This work has deployed a decision tree for defect prediction, but it is often criticized for overfitting. This is one of the limitations of this paper. Apart from this, comparing the predicted defect count with other prediction models hasn’t been attempted. NSGA-II has been applied to solve the optimization problem; however, the optimal results obtained have yet to be compared with other algorithms. Further study is envisaged.

The Pareto front provides an effective visual aid for managers to compare multiple strategies to decide the best possible rework “cost” and “time” for their projects. It is beneficial for cost-sensitive start-ups to estimate the rework “cost” and “time” to negotiate with their customers effectively.

This paper proposes a novel quality management framework under the Six Sigma approach, which integrates optimization of critical metrics. As part of this study, a unique mathematical model of the software defect rework process was developed (combined with the proposed framework) to obtain the optimal solution for the perennial problem of schedule slippage in the rework process of software development.

The success of Six Sigma in manufacturing in the past decade has encouraged moves to explore Six Sigma applications to other domains, such as the software industry, for performance improvement. Owing to the uniqueness of software processes, there have been disagreements as to whether Six Sigma should be adopted in software design processes. In this paper, we discuss the applicability of the Six Sigma framework to software. Some myths and facts about the Six Sigma Software Program (6SSP) are discussed. We also address some common misconceptions on the potential of Six Sigma in software, as well as some actual practical challenges. A framework is suggested for practitioners and managers interested in exploiting the benefits of statistical analysis in general, and 6SSP in particular. Some ideas are also raised on what remains to be done to make 6SSP work.

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 Gray 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. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray 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.

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.

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.

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.

This paper examines the impact of team factors in software development, such as the domain and language experience of the team members and the personnel capability of the team, on the costs and quality of the software products. The measure of the quality of the software products is based on the number of unique field problems that customers reported. The analysis, based on data collected on 37 software projects from a leading firm in the packaged software industry, indicates that software teams with higher levels of personnel capability exhibit significantly higher productivity and quality in the software products they deliver. A case study of one of the most successful package software development efforts at this firm highlights the important aspects of team dynamics in a highly successful software project.

Suggests that, in order to detect and correct software defects as early as possible, identifying and generating more defect‐sensitive test cases for software unit and subsystem testing is one solution. Proposes an orthogonal software testing approach based on the quality optimization techniques, Taguchi methods. This orthogonal approach treats the input parameters of a software unit or subsystem as design factors in an orthogonal arrays, and stratifies input parameter domains into equivalent classes to form levels of factors. Describes how test cases are generated statistically for each trial of factorial orthogonal experiments. The adequacy of the generated test cases can be validated by examining testing coverage metrics. The results of test case executions can be analysed in order to find the sensibility of test cases for detecting defects, to generate more effective test cases in further testing, and to help locate and correct defects in the early stage of testing.

Project managers in information systems play a central role in the development, maintenance, and enhancement of software. Software metrics assist these managers in identifying opportunities for process improvement and help quantify software characteristics. Weaknesses in the traditional approaches to measuring reliability have led to the development of software metrics. The interpretation of software metrics can be critical to making effective responses in the management information systems’ decision‐making processes. This paper gives insight into the use and understanding of some software metrics.

The benefits associated with implementing concurrent engineering mean that more and more companies are looking to use it for their new product introduction process. Previously, methods for monitoring product development have included the use of metrics. “Predictive” metrics have also been used, but have mainly tended to focus on the software and firmware aspects of a product. Outlines metric ideas, but takes a holistic view of the product under development. Details an industrial case study which shows how information obtained during the product development process can be used to produce predictive metrics. These can be used as a means to make the development process more efficient. Discusses the benefits of predictive metrics and the impact that concurrent engineering approach has on the ability to provide these metrics.

Statistical process control (SPC) is a powerful technique for managing, monitoring, analyzing and improving the performance of a process through the use of statistical methods. The purpose of this paper is to present results of a survey on SPC in the software industry. The focus is on understanding the critical success factors (CSFs) for successful implementation of SPC in the software industry.

In total, 12 critical success factors (CSFs) with 36 variables were identified from the literature and discussions with software quality professionals. An e‐mail questionnaire was used to gather the data.

The results reveal that management commitment and involvement are the most critical success factors, followed by selection of control charts. The use of SPC facilitators was found to be the least important factor in successful deployment of SPC in the software industry.

This research project was conducted with a limited number of participants, and was limited to software firms in India; cultural differences in other nations may yield different results.

These results provide an increased understanding of how to better implement SPC in the software industry, and provide managers with improved guidelines for identifying the most important factors that will lead to success.

Indian software companies are leading exporters to Europe and the USA. Considering the growth of the Indian software industry and increased inclination towards acquiring quality certifications and implementing quality management techniques, a better understanding of the implementation of SPC can provide companies with a stronger competitive advantage.