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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 purpose of this paper is to investigate the relationships between some aspect‐oriented metrics and aspect fault proneness, content and fixing effort.

An exploratory case study was conducted using an open source aspect‐oriented software consisting of 76 aspects, and 13 aspect‐oriented metrics were investigated that measure different structural properties of an aspect: size, coupling, cohesion, and inheritance. In addition, different prediction models for aspect fault proneness, content and fixing effort were built using different combinations of metrics' categories.

The results obtained from this study indicate statistically significant correlation between most of the size metrics and aspect fault proneness, content and fixing effort. The cohesion metric was also found to be significantly correlated with the same. Moreover, it was observed that the best accuracy in aspect fault proneness, content and fixing effort prediction can be achieved as a function of some size metrics.

Fault prediction helps software developers to focus their quality assurance activities and to allocate the needed resources for these activities more effectively and efficiently; thus improving software reliability. In literature, some aspect‐oriented metrics have been evaluated for aspect fault proneness prediction, but not for other fault‐related prediction problems such as aspect fault content and fixing effort.

The purpose of this paper is to classify the most important metrics proposed for web information systems, with the aim of offering the user a global vision of the state of the research within this area.

WQM distinguishes three dimensions related to web features, lifecycle processes and quality characteristics. A range of recently published (1992‐2004) works that include web metrics definitions have been studied and classified within this model.

In this work, a global vision of web metrics is provided. Concretely, it was found that about 44 percent of metrics are related to “presentation” and that most metrics (48 percent) are usability metrics. Regarding the life cycle, the majority of metrics are related to operation and maintenance processes. Nevertheless, focusing on metrics validation, it was found that there is not too much work done, with only 3 percent of metrics validated theoretically and 37 percent of metrics validated empirically.

The classification presented tries to facilitate the use and application of web metrics for different kinds of stakeholders (developers, maintainers, etc.) as well as to clarify where web metric definition efforts are centred, and thus where it is necessary to focus future works.

This work tries to cover a deficiency in the web metrics field, where many proposals have been stated but without any kind of rigour and order. Consequently, the application of the proposed metrics is difficult and risky, and it is dangerous to base decisions on their values.

Unless the key measures are identified and managed, the entire concept of measurement would end up making one step forward and two steps sideways. The key is to identify those measures that add value to the measurement approach per se. The present work is yet another contribution to further augment the purpose and spirit of the concept of measurement.

The present research work strives to critically examine the relative importance of software metrics from the standpoint of their utility towards improving business performance. It illustrates how an absolute AHP framework can be formulated for the same.

Criteria with respect to product, process and resource categories have been identified and decision alternatives have been explored in order to devise boundary conditions for classifying software metrics as critical, essential and redundant (CER).

The new industrial era models and analogies are adequate to meet the rigours of the ever‐changing IT world. But, from the measurement and metrics perspective, it has long been a problem of plenty. The current study provides a methodology to streamline the cornucopia of measures into a consolidated definite set of metrics that are more meaningful and useful with respect to the set objectives.

The lineage of measurement thinking is unswervingly attributing to a paradigm shift in the way metrics are evaluated and value creation is perceived and this has resulted in an approach that is more and more compatible with the Pareto principle. The current work adds fuel to such a thought.

Software fault prediction is an important concept that can be applied at an early stage of the software life cycle. Effective prediction of faults may improve the reliability and testability of software systems. As service-oriented architecture (SOA)-based systems become more and more complex, the interaction between participating services increases frequently. The component services may generate enormous reports and fault information. Although considerable research has stressed on developing fault-proneness prediction models in service-oriented systems (SOS) using machine learning (ML) techniques, there has been little work on assessing how effective the source code metrics are for fault prediction. The paper aims to discuss this issue.

In this paper, the authors have proposed a fault prediction framework to investigate fault prediction in SOS using metrics of web services. The effectiveness of the model has been explored by applying six ML techniques, namely, Naïve Bayes, Artificial Networks (ANN), Adaptive Boosting (AdaBoost), decision tree, Random Forests and Support Vector Machine (SVM), along with five feature selection techniques to extract the essential metrics. The authors have explored accuracy, precision, recall, f-measure and receiver operating characteristic curves of the area under curve values as performance measures.

The experimental results show that the proposed system can classify the fault-proneness of web services, whether the service is faulty or non-faulty, as a binary-valued output automatically and effectively.

One possible threat to internal validity in the study is the unknown effects of undiscovered faults. Specifically, the authors have injected possible faults into the classes using Java C3.0 tool and only fixed faults are injected into the classes. However, considering the Java C3.0 community of development, testing and use, the authors can generalize that the undiscovered faults should be few and have less impact on the results presented in this study, and that the results may be limited to the investigated complexity metrics and the used ML techniques.

In the literature, only few studies have been observed to directly concentrate on metrics-based fault-proneness prediction of SOS using ML techniques. However, most of the contributions are regarding the fault prediction of the general systems rather than SOS. A majority of them have considered reliability, changeability, maintainability using a logging/history-based approach and mathematical modeling rather than fault prediction in SOS using metrics. Thus, the authors have extended the above contributions further by applying supervised ML techniques over web services metrics and measured their capability by employing fault injection methods.

Applications of comprehensive Quality Function Deployment (QFD) – or QFD in the broad sense – to strategic management have been known for some time, and its results have been discussed within the international community of QFD specialists. It is therefore tempting to investigate the contribution of combinatory metrics to strategy deployment. Combinatory metrics are constructed upon the capability of QFD to evaluate the deployment topics’ contribution to customers’ needs. They provide a practical means to explain business strategy by “local” metrics that are easily understood and applied by responsible people. Combinatory metrics also point to foundations of QFD that explain how to apply QFD for very complicated environments. This foundation provides techniques and means to work with various influencing factors and conflicting topics. This paper explains the theory as needed for strategy deployment and presents a sample case from a software company.

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.

Resources allocated to software maintenance constitutes a major portion of the total life cycle cost of a system. The enormous effect that this can have on an organization's ability to react to dynamic environments has been the primary motivation for researchers to study issues related to software maintenance. The purpose of this paper is to take stock of the research conducted in this area in order to identify the intellectual trails embodied in the coherent body of knowledge on software maintenance.

An author co‐citation analysis (ACA) involving authors who have made seminal contributions to the field of software maintenance was performed. The data for the study were obtained from the Science Citation Index (SCI) and the Social Sciences Citation Index (SSCI).

The results indicate that most of the software maintenance research has focused on eight areas: Program logic characteristics, Quality of processes/metrics, Effort and productivity issues, Cognitive issues in repair maintenance, Organizational Issues: Strategies for software evolution/maintenance, Object‐oriented (OO) maintenance, Domain specific language issues and Program construction and design. Research limitations/implications – Some of the limitations of this study include: exclusion of data after 2003, giving equal weight for all citations, and implicit assumption that a relationship exists between the citing and cited documents.

The extension of software maintenance research in the areas suggested in this study may lead to new innovations for practice.

The paper not only introduces new methods for meta analysis, it also suggests that opportunities abound for extending the frontiers of software maintenance research, particularly in the context of contemporary software development approaches.

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 purpose of this paper is to present a business process measurement framework for the evaluation of a corpus of business processes modelled in different business process modelling approaches. The results of the application of the proposed measurement framework will serve as a basis for choosing business process modelling approaches.

The approach uses ideas of the goal question metric framework to define metrics for measuring a business process where the metrics answer the questions to achieve the goal. The weighted sum method (WSM) is used to aggregate the measure of attributes of a business process to derive an aggregate measure, and business process modelling approaches are compared based on the evaluation of business process models created in different business process modelling approaches using the aggregate measure.

The proposed measurement framework was applied to a corpus of business process models in different business process modelling approaches and is showed that insight is gained into the effect of business process modelling approach on the maintainability of a business process model. From the results, business process modelling approaches which imbibed the principle of separation of concerns of models, make use of reference or base model for a family of business process variants and promote the reuse of model elements performed highest when their models are evaluated with the proposed measurement framework. The results showed that the applications of the proposed framework proved to be useful for the selection of business process modelling approaches.

The novelty of this work is in the application of WSM to integrate metric of business process models and the evaluation of a corpus of business process models created in different business process modelling approaches using the aggregate measure.