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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.

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.

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.

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.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

The purpose of the paper is to better understand how global boundaries affect global information system (IS) project success and which mediating process variables increase the chance of success.

Based on the literature on IS success and global teams, an input‐process‐output framework is adopted to develop the research model for the study. This research is based on semi‐structured interviews with 22 global IS project managers. An attribution analysis is used to identify common themes and patterns of the interview results.

Global IS project managers identified time separation and cultural differences as the most significant barriers to project success. Our findings suggest that effective teams were able to overcome these barriers to achieve success, but this success was achieved through the implementation of special coordination, communication and cognitive processes tailored to help teams overcome global barriers and through considerable additional cost and effort.

This study furthers understanding of the global boundaries affecting global IS project success and the most effective processes that teams use to overcome global barriers.

Despite the increasing attention to global IS work, there is limited understanding of why and how global IS projects succeed or fail. The present study, investigates not only how multiple global boundary variables (e.g. geographic dispersion, time separation, language differences, cultural differences, etc.) affect IS project success, but also which processes teams use to cope with the challenges presented by these global boundaries.

Buildings are among the biggest contributors to environmental impacts. To achieve energy-saving and decarbonisation objectives while also improving living conditions, it is imperative to undertake large-scale renovations of existing buildings, which constitute the greater part of building stock and have relatively low energy efficiency. However, building renovation projects poses significant challenges owing to the absence of optimised tools and methods for planning and executing renovation works, coupled with the need for a high degree of interaction with occupants.

This paper describes the development of an automated process, based on building information modelling (BIM) and the principal component analysis method, for overcoming building renovation challenges. The process involves the assessment and simulation of renovation scenarios in terms of duration, cost, effort needed and disruptive potential. The proposed process was tested in three case studies; multi-residence apartment buildings comprising different construction components and systems, located in Greece, France and Denmark, on which six different renovation strategies were evaluated using sensitivity analysis.

The developed tool was successfully able to model and simulate the six renovation scenarios across the three demonstration sites. The ability to simulate various renovation scenarios for a given project can help to strategise renovation interventions based on selected key performance indicators as well as their correlation at two different levels: the building level and the renovated surface area level.

The objectives of this paper are twofold: firstly, to present an automated process, using BIM, for evaluating and comparing renovation scenarios in terms of duration, cost, workers needed and disruptive potential; next, to show the subsequent testing of the process and the analysis of its applicability and behaviour when applied on three live demonstration sites located in three different European countries (France, Greece and Denmark), involving six renovation scenarios.

The research on people and project factors is found extensively in general but not specific to software engineering. Secondly, the existing research has not concentrated on the communication and time complexity of the teams on software economics. The purpose this paper is to develop a model to investigate and quantify the impact of time pressure (TP) on software economics through the communication influence of software team sizes (TS).

A research model and five hypotheses are developed based on the gaps in the literature. The data set from International Software Benchmarking Standards Group repository is used for testing the hypotheses.

Important findings include: smaller TS tends to exert less TP on average; TP is directly proportional to software economics, however; and TP does not affect the productivity required for the software.

The study has the following implications: Selection of an appropriate TS for project completion that ensures minimum pressure on team members; and maximize software outcomes in stress-free environment.

This work is useful for organizations carrying out software projects with teamwork. The project managers can benefit from the results while planning the team factors for achieving the project goals.

The results uphold not to exert pressure on the team as it will not only affect the duly completion of the project but also the well-being of employees.

The paper is the first one where the proposition of TP estimation is done using TS and communication complexity, and empirically evaluating the impact of TP on four major software economics are the major key contributions of this research work.

This paper deals with concept of total productive maintenance (TPM) and its implementation approach. It also presents the identification of critical factors for effective implementation of TPM. The reliability analysis identified potential areas where more concentration is required. The application of hypothesis testing in productivity maintenance should be promoted by parametric test and significantly instrumental in explanation of phenomena. It is also indispensable to better understand quality data and provide guidance to production control.

The various critical success factors of TPM implementation has organised into set of eight performance measure and thirty three sub-factors for getting the in-depth details of each indicator. The paper identifies the reliability of these factors and understands the problem with greater clarity and its ramification. Researcher collected responses from forty one manufacturing organisations through structured designed questionnaire. The reliability analysis was carriedout by calculating the value of Cronbach's alpha method. To draw the meaningful conclusions supported by relevant empirical data, provisional formulation is required, and it was carried by hypothesis testing. In this test, samples are taken from a population with known distribution (normal distribution), and a test of population parameters is executed. It determines the relevancy of facts directs the researcher's efforts into productive channels. The statements were hypothetically tested by calculating the arithmetic value of Chi-Square (χ²) and MINITAB-19 software was used for identification of p-value.

This study identified that main factors and sub-factors of TPM which are critical for implementation of TPM. The study also avoids the complexities involved in implementing TPM by reliability analysis. It is found that all identified CSFs are reliable as Cronbach's alpha is above 0.6. The hypothesis testing shows that all alternative hypothesis statements are acceptable as Chi-Square (χ²) value has satisfied the conditions and null hypothesis are true as calculated p-value is less than the 0.05 for eight identified TPM critical factor.

In this paper researcher provides a comprehensive typology of TPM-CSFs, and its ranking and importance in manufacturing sector. The preparedness of such study related to TPM implementation is becoming a major sourcing base for the world and there is a paucity of such studies. Such studies are equally important in a global context.