Software Quality Assessment Research Articles

Software quality assessment model: A new approach for software testing tools

Software quality assessment model: A new approach for software testing tools

Evaluating Thresholds for Object-Oriented Software Metrics

Software metrics measure quantifiable or countable software characteristics. Researchers may apply them to provide better product understanding, evaluate the process effectiveness, and improve the software quality. A threshold is a value that aids the proper interpretation of software measurements; it indicates whether or not a given value represents a quality risk. Thresholds are unknown for most software metrics, inhibiting their use in a software quality assessment process. In a previous paper, we proposed a catalog with 18 object-oriented software metrics thresholds, providing a preliminary case study in proprietary software to validate them. This article evaluates these thresholds more deeply, considering significant aspects. We show a new example of threshold derivation, discussing it qualitatively. We explain these software metrics and discuss their threshold values, presenting each one’s application level, definition, formula, and implications for the software design. We conduct a study with two software systems to evaluate the capacity of our thresholds to identify software quality enhancement after a restructuring process. We assess these thresholds using two case studies, comparing the evaluation provided by the thresholds with the qualitative analysis given by manual inspections. The study results indicate that the thresholds may lead to few false-positive and false-negative occurrences, i.e., the thresholds provide a proper quantitative assessment of software quality. This study contributes with empirical evidence that the metrics’ thresholds proposed in our previous work provide a proper interpretation of software metrics and, hence, may aid the application of software metrics in practice.

Software Reliability Prediction by Adaptive Gated Recurrent Unit‐Based Encoder‐Decoder Model With Ensemble Empirical Mode Decomposition

ABSTRACTAccurate software reliability prediction is significant to software quality assurance. However, the rapid development and evolution of modern software pose more challenges for accurate software quality assessment. With the rapid development of machine learning and intelligent algorithms, data‐driven nonparametric models have gradually attracted increasing attention with outstanding prediction performance. However, we found that there may exist some prediction lags caused by the autocorrelation and nonstationarity of software fault data in the prediction of nonparametric models affecting their performance. To address this problem, we proposed an adaptive gated recurrent unit‐based encoder‐decoder model (ED‐GRU) with ensemble empirical mode decomposition (EEMD), effectively reducing prediction lags and performing accurate software fault number prediction. The autocorrelation and nonstationarity of fault data are first reduced by using first‐order difference and EEMD to clearly characterize the changing trend of the data. The frequency‐specific ED‐GRU networks are then combined to adaptively learn the nonlinear fluctuation trend of fault data under different frequency scales and obtain accurate prediction final results after aggregation. Experiments on eight public datasets showed that the proposed EEMD‐ED‐GRU‐PF model could effectively reduce the prediction lags and achieve the best prediction performance compared with four nonparametric and five parametric baseline methods in all datasets. Therefore, the proposed method can effectively and stably reduce the prediction lag to significantly improve the prediction accuracy. In this way, developers can accurately evaluate the current quality of the software and provide valuable guidance for software development and maintenance.

Towards a Software Quality Factor Assessment Model for Learning Management Systems (LMS)

Objectives: Software quality models are well-accepted ways to control and guide software quality to enhance user effectiveness and work productivity. However, an adequate understanding of software quality assessment factors that promote the effective operation of Learning Management Systems (LMS) among users in educational contexts needs to be adequate. This study aimed to identify from the literature quality factors, sub-quality factors of the quality factors and their meanings from mainly used software quality factor models, explain Ehlers's Model of subjective quality requirements of e-learning systems and match the quality factors with Ehlers's model and develop a software quality model for e-learning. Methods: Drawing on software quality literature and Ehlers' Quality Model for educational technologies, we propose an LMS Software Quality Factor Model for assessing LMS effectiveness among users in educational contexts. Findings: The proposed model highlights relevant user-centred parameters (predominantly, "Reliability", "Efficiency", and "Flexibility/Evolvability") that ensure LMS quality and effectiveness among users. Novelty : This research is novel from the point of view of determining quality factors for LMS using an existing e-learning model, Ehlers’s user-based e-learning requirement model in this particular instant. Keywords: Software quality factors, LMS, Ehlers's Model, ISO 9126, Educational technologies

Tools for monitoring software quality in information systems development and maintenance: five key challenges and a design proposal

As software grows in size and complexity, organizations increasingly apply tools to automatically assess software quality of information systems during development and maintenance. Software quality assessment tools (SWQAT) promise fast and actionable insights into the technical state of software through various quality characteristics, such as maintainability, reliability, or security. These tools have been used to support a wide variety of Information Technology (IT) project management decisions related to system development, contract negotiations, project terminations, and even settling legal disputes between suppliers and clients. However, despite their rising importance, questions regarding how they function and how reliable they are to support decision-making have so far escaped scholarly attention. This paper conducts an evaluation of widely used SWQATs and analyzes how they rate the quality of software systems of varying sizes, functionalities, and programming languages. Our results reveal five key challenges for using SWQATs in IT projects. To address these challenges, we propose a design for tailorable SWQATs that allows for more conscious and prudent software quality assessments that better reflect the socio-technical aspect of software systems and the context-specific nature of software quality.

Software Quality Assurance as a Service: Encompassing the quality assessment of software and services

This paper introduces the Software Quality Assurance as a Service (SQAaaS) concept and it describes an open-source implementation of a comprehensive platform that supports the automated assessment of specific quality metrics for software and services, defined as a set of baseline requirements. The platform is openly accessible, focuses on research software and open science, and promotes best practices by awarding standards-based digital badges to software and services. It provides an easy-to-use web-based graphical user interface which facilitates the interaction with server-side components in charge of automatically creating CI/CD (Continuous Integration/Continuous Delivery) pipelines for automated testing of the baseline criteria. The service is in production and has performed over 2800 assessments, awarding more than 125 digital badges across several scientific disciplines.

Data-oriented QMOOD model for quality assessment of multi-client software applications

There has been a great effort to evaluate software quality using proper tools and methods against different development environments changing over time. Quality Model for Object Oriented Design (QMOOD) is a verified model used for quality assessment of object-oriented software. The model associates quality metrics gathered from the source code and quality attributes in use to present a quality measurement. However, the model should be revised for recent multi-client software including native client applications, because there is a deficiency of metric gathering tools in such environments. More specifically, it is sometimes not possible to gather all quality properties required by QMOOD in all native development platforms of client applications. Hence, even though different client applications have the same design, the implementation quality cannot be monitored for the quality assurance. Analyzing and simplifying the metric set may alleviate this challenge, and a convenient quality assessment might be achieved. Thus, we propose to change the operational aspect of QMOOD by inserting an additional layer, Data Analytic, to the hierarchical structure of the conventional model. Accordingly, we provide a discussion on a case study including five native client applications. For this purpose, a design quality of one of the client applications is achieved to validate the appropriateness of the design, the data analytic on the metric set are implemented and the proposed data-oriented simplified QMOOD is applied to the other client applications. Finally, it is stated that the proposed approach successfully alleviated the problems in metric gathering for multi-client applications while applying QMOOD.

Design and development of software quality forensics framework and model

The illicit activist tampers the software for malcious purposes. The process of forensics examining and gathering evidence for software accused in forensic laboratories presents challenges in terms of software quality. Therefore, it is crucial to design and develop a framework, model, and dedicated standards for the forensic analysis of software quality. These resources can be employed for investigations, legal proceedings, as well as establishing criteria and standards for both past and present scenarios. The aim of the research work is to design a software-quality forensics framework for forensic investigation to the quality of software. The research primarily concentrates on developing a model for software quality assessment and identifying the pertinent existing standards through the conceptual framework of software quality forensics. The implementation of software quality forensics has improved the efficiency and effectiveness in investigating and managing incidents related to software quality. The proposed framework for software quality forensics allows for the evaluation and assessment of quality issues within specific software systems. To complement the software quality forensic framework, a comparative analysis has been conducted between existing software quality models and the proposed software quality forensic model.

Software Quality Assessment Technique for the Autonomous Power Plants Automated Control Systems

Software Quality Assessment Technique for the Autonomous Power Plants Automated Control Systems

EXAMINING SOFTWARE QUALITY CONCEPT: BUSINESS ANALYSIS PERSPECTIVE

Software quality is a critical aspect of software development that significantly impacts business performance and customer satisfaction. However, defining software quality can be challenging, as different sources provide various definitions and perspectives. The article presents a literature review of software quality, acknowledging an ongoing debate over the years regarding the definition of software quality and the methods used for its assessment. Among all the different ideas about software quality, the article highlights key concepts that are crucial in understanding software quality: meeting requirements, satisfying users, using software features, and spotting defects. The article also checks out international standards like ISO/IEC 25010:2011 and ISO/IEC 5055:2021, introducing terms such as "Quality in use" and "Structural Quality." Unveiling a tripartite perspective elucidated in international standards—internal quality, external quality, and quality in use - the article underscores the intricate interplay between subjectivity and objectivity. The subjective dimension, influenced by user perception and contextual factors, is juxtaposed with more objective criteria such as conformance to requirements and the absence of defects. The standards provide helpful perspectives, but the human side of things, like user feelings and specific contexts, makes finding a universal definition tricky. The pivotal role of business analysis and requirements engineering in ensuring software quality is underscored. Business requirements, stakeholder needs, and the quality of functional and non-functional requirements emerge as integral components. The article argues that software quality is intricately tied to the quality of its requirements, presenting a dual perspective: compliance with quality criteria and alignment with stakeholders' expectations and business goals. Practical software quality assessment is built upon the foundational understanding of contextual nuances, user needs, and operational conditions, all discerned through business analysis.

Application of Meta-Programming Techniques for Accelerating Software Development and Improving Quality

A contemporary software tool has been devised to evaluate software quality through metric analysis techniques. This tool calculates pertinent metrics utilizing quality indicators and establishes a composite quality indicator value for software products. The intricacies of software quality assessment processes have been elucidated, including the examination of software quality's standardization as well as the presentation level of its model. This enables the potential for enhancement through the formulation of suitable criteria for quality assessment, refining models for metric analysis, and quantitatively measuring quality across all phases of project implementation. Notably, the use of metric analysis to gauge software quality reveals a lack of standardized metrics, resulting in varying assessment methods and metrics from different measurement system providers. Interpreting metric values also proves challenging for most software users due to a lack of clarity and informativeness. Furthermore, it has been discovered that while decisions based on cost, development duration, and designer company reputation influence software implementation choices, they do not always guarantee optimal software quality.

A study on software development for comprehensive assessment of English online teaching quality in universities based on deep learning

Abstract In this paper, a deep learning ID3 algorithm based on a decision tree generation algorithm is proposed and applied to the comprehensive assessment of English online teaching quality. Secondly, the structure and design method of the data warehouse of the comprehensive assessment software of online teaching quality is also constructed, including the design of the operating system interface and the data warehouse itself. Finally, the application results and analysis of the English web-based comprehensive teaching quality assessment software were conducted through assessment attributes and network training. The results show that the correlation coefficient between the two is 0.9348 from the evaluation results of the sample and 0.9116 from the analysis of the network generalization performance test. The correlation coefficient between the two is close to completely accurate from the evaluation and test results. The software developed in this paper provides a certain reference value for achieving the improvement and optimization of teaching quality.

SQMetrics: An Educational Software Quality Assessment Tool for Java

Over the years, various software quality measurement models have been proposed and used in academia and the software industry to assess the quality of produced code and to obtain guidelines for its improvement. In this article, we describe the design and functionality of SQMetrics, a tool for calculating object-oriented quality metrics for projects written in Java. SQMetrics provides the convenience of measuring small code, mainly covering academic or research needs. In this context, the application can be used by students of software engineering courses to make measurements and comparisons in their projects and gradually increase their quality by improving the calculated metrics. Teachers, on the other hand, can use SQMetrics to evaluate students’ Java projects and grade them in proportion to their quality. The contribution of the proposed tool is three-fold, as it has been: (a) tested for its completeness and functionality by comparing it with widely known similar tools, (b) evaluated for its usability and value as a learning aid by students, and (c) statistically tested for its value as a teachers’ aid assisting in the evaluation of student projects. Our findings verify SQMetrics’ effectiveness in helping software engineering students learn critical concepts and improve the quality of their code, as well as in helping teachers assess the quality of students’ Java projects and make more informed grading decisions.

Software quality models: Exploratory review

INTRODUCTION: The emerging industrialization of software promotes the continuous development of new evaluation models that adapt to the product's characteristics and the organizations' needs. OBJECTIVES: This article aims to identify the software quality models proposed between 2016 and 2020. METHODS: We conducted an exploratory systematic review in the SciELO, IEEE Xplore, and Scopus databases, resulting in 14 research papers proposing new models for assessing software quality. RESULTS: 79% of the research papers were extracted from IEEE Xplore, and 50% of them were authored by individuals affiliated with European institutions. We presented various software quality models focused on assessing both product quality and usability for specific purposes. Additionally, we reported that the proposed models are based on common software quality metrics standards such as CMMI, ISO/IEC 9126, and others. CONCLUSION: The review serves to update the state of the art regarding the software quality models proposed in recent years, which will be valuable for researchers and software developers when seeking evaluation models that align with their specific needs.

Construction of similarity measure for intuitionistic fuzzy sets and its application in face recognition and software quality evaluation

As a generalization of fuzzy sets, intuitionistic fuzzy sets (IFSs) are more capable of representing and addressing uncertainty in real-world problems. As a result, IFSs have been utilized in various areas of application. However, the distance and similarity measures between the two IFSs are still an open issue that has drawn much attention over the past few decades. Even though several intuitionistic fuzzy similarity measures (IFSMs) have been developed, a number of issues still exist, including counter-intuitive results, ‘the zero divisor problem,’ violation of similarity measure axioms, and being incompetent to detect minor changes in membership or non-membership. To overcome these shortcomings, a novel intuitionistic fuzzy similarity measure (IFSM) has been introduced in this study. Unlike existing measures, this method considered the global maximum and minimum of differences in memberships and differences in non-memberships, along with their individual differences, to construct an IFSM. Moreover, it has been shown that a convex combination of two similarity measures is also a similarity measure. Some numerical examples are employed to emphasize the advantages and strengths of the proposed method over existing ones. The suggested IFSM has been implemented on a few pattern classification issues to demonstrate its efficacy and a new face recognition method is also presented. Moreover, an entropy measure is introduced using the proposed IFSM, which is further used to construct the weights of attributes in the MADM method. Furthermore, the MADM technique, IF-E-TOPSIS, is constructed using the suggested IFSM and entropy measure. The effectiveness of this method is shown by utilizing it for software quality assessment, and the results are compared with existing ones to highlight the superiority of the proposed method.

Practical Consequences of Quality Views in Assessing Software Quality

The authors’ previously published research delved into the theory of software product quality modelling, model views, concepts, and terminologies. They also analysed this specific field from the point of view of uncertainty, and possible descriptions based on fuzzy set theory and fuzzy logic. Laying a theoretical foundation was necessary; however, software professionals need a more tangible and practical approach for their everyday work. Consequently, the authors devote this paper to filling in this gap; it aims to illustrate how to interpret and utilise the previous findings, including the established taxonomy of the software product quality models. The developed fuzzy model’s simplification is also presented with a Generalized Additive Model approximation. The paper does not require any formal knowledge of uncertainty computations and reasoning under uncertainty, nor does it need a deep understanding of quality modelling in terms of terminology, concepts, and meta-models, which were necessary to prepare the taxonomy and relevance ranking. The paper investigates how to determine the validity of statements based on a given software product quality model; moreover, it considers how to tailor and adjust quality models to the particular project’s needs. The paper also describes how to apply different software product quality models for different quality views to take advantage of the automation potential offered for the measurement and assessment of source code quality. Furthermore, frequent pitfalls are illustrated with their corresponding resolutions, including an unmeasured quality property that is found to be important and needs to be included in the measurement and assessment process.

MATHEMATICAL MODELLING OF THE PROCESS OF QUALITY CONTROL OF CONSTRUCTION PRODUCTS

The study's abstract summarizes the findings of years of research in the area of quality management of industrial production and construction production, which were based on mathematical modeling techniques, process, and outcomes of implementing the developed program of monitoring and quality control in the enterprise's production process. The objective of this work is to show to the scientific community the useful outcomes of mathematical modeling in software applications. Quality control and safety represent increasingly important concerns for project managers. Defects or failures in constructed facilities can result in very large costs. Even with minor defects, re-construction may be required and facility operations impaired. Increased costs and delays are the result. In the worst case, failures may cause personal injuries or fatalities. Accidents during the construction process can similarly result in personal injuries and large costs. Indirect costs of insurance, inspection and regulation are increasing rapidly due to these increased direct costs. Good project managers try to ensure that the job is done right the first time and that no major accidents occur on the project. The research focused on describing applicable mathematical models, viewpoints, and practical outcomes of their use in the applied sector to evaluate quality control. This mathematical model was applied by the authors. The outcomes of using this approach are presented in the article. By utilizing mathematical modeling techniques, the authors created an experimental software management and quality assessment system. The authors continue their study in this area to enhance prognostic and diagnostic processes based on mathematical modeling methodologies for diagnostic systems and quality management systems.. The independent quality control, which was based on some traditional inspection techniques and used mathematical modeling techniques, revealed a negligible partial discrepancy in the results of the estimation, proving both the efficacy of the product and the suitability of the mathematical modeling concept. Keywords: Quality control, mathematical modeling, construction quality.

Continuous Assessment and Improvement of Software Quality with DevOps-Based Hybrid Model of Automation Tools

Continuous Assessment and Improvement of Software Quality with DevOps-Based Hybrid Model of Automation Tools

Review of Software Quality Verification and Evaluation Methods

The basic concepts in the field of software quality testing and evaluation are defined. In particular, the concepts of verification, validation and software lifecycle and their role in the testing process are considered. The concept of an artifact in the software lifecycle is considered. The Russian and international standards in the field of software quality assessment are taken into account. The existing methods of software verification are regarded: expertise, static analysis, formal methods, dynamic and synthetic methods.An overview of software quality assessment methods used in software development and testing cycles is presented. Functional and administrative factors affecting software quality are also considered. The article lists modern testing automation tools that can be used today. The review material of the article will be useful for software developers, novice testers and related specialists.

On the Impact of Grading on Teamwork Quality in a Software Engineering Capstone Course

Every semester, we deliver a capstone course on software engineering where students undertake a real-world project in three iterations. By the end of each iteration, students are graded in several dimensions: software quality, project management, and peer assessment. The latter is the only grade assigned individually; therefore, students who are penalized by their teams (e.g., for being perceived as low contributors to the team effort) are not severely affected. This results in little incentive for improvement, which potentially jeopardizes the overall quality of the project outcome. Envisaging to promote team cohesion, we devised a new grading rule: if the peer assessment of a student is lower than a threshold, that would be their final grade in the iteration. This paper reports the results of studying the effectiveness of the proposed rule. We recorded peer assessments over six consecutive semesters: (1) the first three as the baseline measure; (2) the semester where we introduced the new grading rule; and (3) the following two semesters, as a contrast. When the rule was first introduced, peer assessments resulted low and heavily spread at the beginning, but they consistently improved toward the end of the semester. When the instructional team already trusted the rule and explicitly emphasized its application, peer assessments consistently grew along the semester but resulted in fewer outliers. The study results show that exposing peer assessments earlier on helps promote team reflection. They also made evident the positive impact of teamwork for producing quality products in a software engineering capstone course.