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Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fault-proneness: the Chidamber and Kemerer (CK) metrics, Abreu's Metrics for Object-Oriented Design (MOOD), and Bansiya and Davis' Quality Metrics for Object-Oriented Design (QMOOD). Some CK class metrics have previously been shown to be good predictors of initial OO software quality. However, the other two suites have not been heavily validated except by their original proposers. Here, we explore the ability of these three metrics suites to predict fault-prone classes using defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. We conclude that the CK and QMOOD suites contain similar components and produce statistical models that are effective in detecting error-prone classes. We also conclude that the class components in the MOOD metrics suite are not good class fault-proneness predictors. Analyzing multivariate binary logistic regression models across six Rhino versions indicates these models may be useful in assessing quality in OO classes produced using modern highly iterative or agile software development processes.

In software, competition in producing high-quality products has become a prominent factor for business success. In this regard, identifying and defining software quality metrics (SQM) to discover and continuously enhance current quality systems is very important. However, it is advisable to study and review current studies in this field, so that it is possible to analyze the current situation, and it also enables us to formulate expectations regarding future research areas. This research is concerned with studying and analyzing a large number of articles, focusing on the research literature published over the past decade. 70 research papers, articles, and conference papers were selected and analyzed, published from 2009 to 2023. A detailed description of these researches and their titles SQM was conducted. We used graphics, explanations, and structure design to display the results. The outputs from this research indicate the underlying knowledge in this field and the measurement mechanism and include trends between 2009 and 2023 and the gaps that are supposed to be available for study and development in this field. The study and analysis of articles aim to review studies, direct future studies, and focus on system development. Future studies encourage the adoption of quality metrics. Quality metrics include several areas of development systems, including network performance, and the Cloud of Things, which directs the adoption of more accurate metrics and components reusability, artificial intelligence, model performance, and predictive capacity metrics.

Software stands out as one of the most rapidly evolving technologies in the present era, characterized by its swift expansion in both scale and complexity, which leads to challenges in quality assurance. Software defect prediction (SDP) has emerged as a methodology crafted to anticipate undiscovered defects, leveraging known defect data from existing codes. This methodology serves to facilitate software quality management, thereby ensuring overall product quality. The methodologies of machine learning (ML) and one of its branches, deep learning (DL), exhibit superior accuracy and adaptability compared to traditional statistical approaches, catalyzing active research in this domain. However, it makes it hard to generalize, not only because of the disparity between open-source projects and commercial projects but also due to the differences in each industrial sector. Consequently, further research utilizing datasets sourced from diverse real-world sectors has become imperative to bolster the applicability of these findings. For this study, we utilized embedded software for use with the telecommunication systems of Samsung Electronics, supplemented by the introduction of nine novel features to train the model, and a subsequent analysis of the results ensued. The experimental outcomes revealed that the F-measurement metric has been enhanced from 0.58 to 0.63 upon integration of the new features, thereby signifying a performance augmentation of 8.62%. This case study is anticipated to contribute to bolstering the application of SDP methodologies within analogous industrial sectors.

Technical debt is considered detrimental to the long-term success of software development, but despite the numerous studies in the literature, there are still many aspects that need to be investigated for a better understanding of it. In particular, the main problems that hinder its complete understanding are the absence of a clear definition and a model for its identification, management, and forecasting. Focusing on forecasting technical debt, there is a growing notion that preventing technical debt build-up allows you to identify and address the riskiest debt items for the project before they can permanently compromise it. However, despite this high relevance, the forecast of technical debt is still little explored. To this end, this study aims to evaluate whether the quality metrics of a software system can be useful for the correct prediction of the technical debt. Therefore, the data related to the quality metrics of 8 different open-source software systems were analyzed and supplied as input to multiple machine learning algorithms to perform the prediction of the technical debt. In addition, several partitions of the initial dataset were evaluated to assess whether prediction performance could be improved by performing a data selection. The results obtained show good forecasting performance and the proposed document provides a useful approach to understanding the overall phenomenon of technical debt for practical purposes.

In modern organizational environments, project management information systems (PMIS) play an important role in ensuring project success with the user requirements, keeping overall costs within the planned budget, and delivering projects at the agreed time. Selecting a high-quality PMIS is vital for the success of project management. A software quality model tailored to PMIS, summarizing the intangible software quality metrics (ISQM) that are effective in evaluating a PMIS, can help better decision-making on PMIS for project managers. However, there is limited research on the PMIS-tailored quality model. To fill the gap, this study evaluates effective SQM for PMIS quality assessment. There are two types of PMIS: Web-based PMIS and PMIS software applications. To narrow the context of PMIS, we focus on web-based PMIS since they are widely used across the industry, such as Microsoft Project and Jira. According to the PMIS features, we merely explored the tailored quality models that have been proven to be more appropriate for web-based PMIS rather than the basic models, such as ISO/IEC 9126, ISO/IEC 25010, and Bertoa. This research uses a qualitative approach to conduct the commonality screening among these models and find out the key evaluation metrics, such as usability and functionality, and the corresponding qualitative attributes suitable for Web-based PMIS quality assessment. The selected metrics and attributes are used to form a Web-based PMIS-tailored quality model. The scoring mechanism is introduced based on the PMIS-tailored quality model, where project managers can have a clear comparison among different web-based PMISs, leading to effective web-based PMIS selection for project management.

Following up on the issue of programmer knowledge profiles, the present article deals with the generation of profiles for C language programmers. The main idea is based on static analysis of source codes, assembling the most significant data about their author. Such a profile can point out some abilities and habits of the programmer. The process of static analysis applies methods and techniques of size metrics, complexity metrics, and clone detection. We also present an experiment focused on novice programmers. The results show that knowledge profiles provide an easy way of novice programmers’ progress tracking. The main contribution is believed to be in the area of program comprehension.

Deoxyribonucleic acid (DNA) sequencing is the process of locating the sequence of the main chemical bases in the DNA. Next-generation sequencing (NGS) is the state-of-the-art DNA sequencing technique. The NGS technique advanced the biological science in analyzing human DNA due to its scalability, high throughput, and speed. Analyzing human DNA is crucial to determine the ability of a person to develop certain diseases and his ability to respond to certain medications. ChIP-sequencing is a method that combines chromatin immunoprecipitation (ChIP) with NGS sequencing to analyze protein interactions with DNA to identify binding sites. Many online web tools have been developed to conduct ChIP-Seq data analysis to either discover or find motifs, i.e., patterns of binding sites. Since these ChIP-Seq web tools need to be used by clinical practitioners, they must comply to the web-related usability tasks including effectiveness, efficiency and satisfaction to enhance the user experience (UX). To that end, we have conducted an empirical study to understand their UX design. Specifically, we have evaluated the usability of 8 widely used ChIP-Seq web tools against 6 known usability quality metrics. Our study shows that the design of the studied ChIP-Seq web tools does not follow the UX design principles.

Reusability is necessary for developing advance software. Aspect Oriented programming is an emerging approach which understand the problem of arrangement of scattered software modules and tangled code. The aim of this paper is to explore the AOP approach with implementation of real life projects in AspectJ language and its impact on software quality in form of reusability. In this paper, experimental results are evaluated of 11 projects (Java and AspectJ) using proposed Quality Evaluation Model for Software Reuse (QEMSR) and existing Aspect Oriented Software Quality Model (AOSQ). To evaluate AOP quality model QEMSR based on developers AOP projects by using Analytic Hierarchy Process (AHP) tools. Paper provides the evaluation of software reusability and positive impact on software quality. QEMSR model is used to assess Aspect Oriented reusability quality issues, which helps developers to adapt for software development. The overall quality of three models QEMSR, existing AOSQ and PAOSQMO are 0.62552223, 0.5283693, and 0.505815 calculated. According to this, QEMSR model is best in form of quality in same characteristics and sub-characteristics.

Among the key factors for the success of a metrics program are the regularity of metrics collection, a seamless and efficient data collection methodology, and the presence of non-intrusive automated data collection tools. This paper presents the software process data warehousing architecture SPDW+ as a solution to the frequent, seamless, and automated capturing of software quality metrics, and their integration in a central repository for a full range of analyses. The striking features of the SPDW+ ETL (data extraction, transformation, and loading) approach are that it addresses heterogeneity issues related to the software development context, it is automatable and non-intrusive, and it allows different capturing frequency and latency strategies, hence allowing both analysis and monitoring of software metrics. The paper also provides a reference framework that details three orthogonal dimensions for considering ETL issues in the software development process context, used to develop SPDW+ ETL. The advantages of SPDW+ are: (1) flexibility to meet the requirements of the frequent changes in SDP environments; (2) support for monitoring, which implies the execution of frequent and incremental loads; (3) automation of the complex and time-consuming task of capturing metrics, making it seamless; (4) freedom of choice regarding management models and support tools used in projects; and (5) cohesion and consistency of the information stored in the metrics repository which will be used to compare data of different projects. The paper presents the reference framework, illustrates the key role played by the metrics capturing process in a metrics program using a case study, and presents the striking features of SPDW+ and its ETL approach, as well as an evaluation based on a prototype implementation.

In this paper, we combine the complex network theory and the traditional software structure metrology to propose a new model for the study of the structural characteristics of the software--- Multi-dimensional measurement model of the software structure properties. The multi-dimensional measurement model of the software structure properties is divided into three parts. Each part has their own properties. In this model, the system is abstracted into a network model in the first step. Then we design the metric parameters considering both the complex network theory and the Object-Oriented software research and also give the definition and calculation method of these metric parameters. And on this basis we use the advantages of eclipse and complex network simulation tool pajek to calculate the metrics parameters designed before. Then give explanation to the experimental results which can demonstrate the reliability of the new model, which has also made a solid foundation for the following study of the software structure properties.