Software Quality Attributes Research Articles

Quality attributes of software architecture in IoT-based agricultural systems

Software architecture forms the cornerstone for achieving and ensuring various software quality attributes. It encompasses the collected requirements of the product, serving as a blueprint that delineates quality features for all project stakeholders, along with methods for measurement and control. Despite the significant increase in IoT-based agricultural systems, there is a dearth of studies on the quality elements of their software architecture. To address this need, this study offers an overview of components and services tailored to address specific quality attributes pertinent to agriculture systems. It identifies, investigates, and presents quality attributes influencing the design of software architecture for IoT-based agriculture systems. This paper identified and discussed several quality attributes, including performance, scalability, flexibility, interoperability, productivity, extensibility, and security, and mapped them to corresponding components of the IoT-based agriculture software architecture. Also, several issues were identified and discussed for the software architecture quality of IoT-based agriculture systems, such as real-time processing and interoperability due to the various devices and protocols utilized in these systems. The findings of this study offer valuable insights for developing, executing, and refining IoT-based agricultural systems to fulfill the changing requirements of the agriculture industry.

IDENTIFICATION OF SOFTWARE QUALITY ATTRIBUTES FROM CODE DEFECT PREDICTION: A SYSTEMATIC LITERATURE REVIEW

Identifying and understanding reasons for deriving software development defects is crucial for ensuring software product quality attributes such as maintainability. This paper presents a systematic literature review and the objective is to analyze the suggestions of other authors regarding software code defect prediction using machine learning, deep learning, or other artificial intelligence methods for the identification of software quality. The systemic literature review reveals that many analyzed papers considered multiple software code defects, but they were analyzed individually. However, more is needed to identify software quality attributes. The more profound analysis of code smells indicates the significance when considering multiple detected code smells and their interconnectedness; it helps to identify the software quality sub-attributes of maintainability.

Identifying the Software Quality Attributes that Affect Patient Portal Adoption in Trinidad and Tobago

Identifying the Software Quality Attributes that Affect Patient Portal Adoption in Trinidad and Tobago

How accessibility affects other quality attributes of software? A case study of GitHub

How accessibility affects other quality attributes of software? A case study of GitHub

Code Smell Detection using Machine Learning Classification Algorithm

Code smell indicates a poor implementation choice that affects software quality attributes (Pérez, 2013). Fowler (1999) also describes it as an internal code-level problem where the code becomes complex, the design broken, and eventually worsens software quality. Jose (2020) has reported that most applied existing approaches for code smells detection are search-based (30.1%), metric-based (24.1%), and symptom-based approaches (19.3%). However, these existing approaches can only apply to simpler detection; the greater the complexity of code smell, the lower the results for code smell detection (Mantyla M, 2004). Kessentini (2014) also has reported that detecting the problems of code smell is difficult and the performance is not effective using the existing approaches such as search-based, symptom-based, visualization-based, probabilistic, cooperative-based, manual, metrics-based, and rule-based. As a result, many of these approaches extend to the application of machine learning classifiers in software code smell detection. Fontana (2016) reported that a supervised machine learning strategy can be used to forecast the value of the dependent variable using machine learning classifiers to address the problem. In this project, we propose a machine learning supervised Gaussian processes algorithm for JAVA open-source code smell detection. The Gaussian process is a highly interpretable supervised machine learning algorithm used in regression testing to quantify prediction uncertainty. A code smell detection application prototype will be developed to implement the proposed work. The effectiveness of the proposed work in terms of detection accuracy will be evaluated further.

Developers talking about code quality

There are many aspects of code quality, some of which are difficult to capture or to measure. Despite the importance of software quality, there is a lack of commonly accepted measures or indicators for code quality that can be linked to quality attributes. We investigate software developers’ perceptions of source code quality and the practices they recommend to achieve these qualities. We analyze data from semi-structured interviews with 34 professional software developers, programming teachers and students from Europe and the U.S. For the interviews, participants were asked to bring code examples to exemplify what they consider good and bad code, respectively. Readability and structure were used most commonly as defining properties for quality code. Together with documentation, they were also suggested as the most common target properties for quality improvement. When discussing actual code, developers focused on structure, comprehensibility and readability as quality properties. When analyzing relationships between properties, the most commonly talked about target property was comprehensibility. Documentation, structure and readability were named most frequently as source properties to achieve good comprehensibility. Some of the most important source code properties contributing to code quality as perceived by developers lack clear definitions and are difficult to capture. More research is therefore necessary to measure the structure, comprehensibility and readability of code in ways that matter for developers and to relate these measures of code structure, comprehensibility and readability to common software quality attributes.

Machine Learning-Based Exploration of the Impact of Move Method Refactoring on Object-Oriented Software Quality Attributes

Refactoring is a maintenance task that aims at enhancing the quality of a software’s source code by restructuring it without affecting the external behavior. Move method refactoring (MMR) involves reallocating a method by moving it from one class to the class in which the method is used most. Several studies have been performed to explore the impact of MMR on several quality attributes. However, these studies have several limitations related to the applied approaches, considered quality attributes, and size of the selected datasets. This paper reports an empirical study that applies statistical and machine learning (ML) approaches to explore the impact of MMR on code quality. The study overcame the limitations of the existing studies, and this improvement is expected to make the results of this study more reliable and trustworthy. We considered eight quality attributes and thirty quality measures, and a total of approximately 4 K classes from seven Java open-source systems were involved in the study. The results provide evidence that most of the quality attributes were significantly improved by MMR in most cases. In addition, the results show that a limited number of measures, when considered individually, have a significant ability to predict MMR, whereas most of the considered measures, when considered together, significantly contribute to the MMR prediction model. The constructed ML-based prediction model has an area under curve (AUC) value of 96.6%.

Quality Measurement of Accurate-5 Accounting Software Using the Iso 9126 Model

This study aims to evaluate the degree of excellence of the Accurate-5 accounting software by employing the ISO-9126 model, which assesses six dimensions (primary characteristics) and associated attributes (secondary characteristics) of software quality. The researchers measured a sample of 41 participants who attended a workshop at STMIK Mardira Indonesia Bandung using the Accurate-5 software. The researchers gathered primary data using a questionnaire that utilized the Likert scale. The validity test results supported the validity of the items, as a value greater than 0.316 was found. The reliability test results demonstrated that all attributes have a high level of reliability, as each item had a Cronbach's alpha value greater than 0.7. The software quality evaluation findings utilizing the ISO 9126 standard indicate that all dimensions and attributes satisfy the "Good" criteria. The researchers found that the attributes of Compliance, Installability, and Coexistence satisfy the criteria of "Very Good." Based on the data, we can infer that the Accurate-5 software complies with the ISO 9126 standard as it achieved an average percentage score of 77.53%, indicating that it meets the "Good" criteria.

Quality Analysis Framework based on Complexity for Change Management Using Intelligent Digital Twin

Software attributes analysis in industrial automation systems due to the higher proportion of using software-defined systems is highly significant. The requirement of higher flexibility, adaptivity, quality, less cost, and time to market as main classes of manufacturing attributes in dynamic and heterogeneous environments induce a large amount of complexity, which directly deteriorates the external software quality attributes. Complexity is a comprehension of quality management factor that influences the quality attributes of an asset such as testability, reusability, reliability, and maintainability. In order to manage the continuous requirement of software-defined systems’ updating efficiently, an automatic method for estimating these quality attributes is required. This paper proposes a novel approach for quality attributes analysis with the factor of complexity estimation. The proposed approach is realized in a framework that exploits the operational simulation capability of the intelligent Digital Twin concept to decrease the cost of untoward consequences of complexity increase on software external attributes by using operational data. It uses the simulated asset to predict how complexity due to applying new changes would impact the software quality attributes. The quality analysis process contains the intelligence process of monitoring the complexity metrics vector and quality attributes vector, the analysis of training models, the prediction of attributes, and the evaluation of measurements to enhance the quality attributes in advance. A comprehensive evaluation using the example of a Robotino 3 Premium from Festo, an autonomous mobile robot, is used to show the consequences of changes in software quality attributes for validation of the proposed framework.

Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

Refactoring is one of the most widely used techniques in practice to improve the quality of existing software. However, it is observed that refactoring does not continually improve all software quality attributes. Recent studies indicated that different refactoring techniques have significantly different, sometimes opposite, and conflicting effects on software quality attributes. In other words, there is contradictory evidence on the refactoring benefit. As a result, developers face challenges in selecting appropriate refactoring techniques when they use them to improve software quality. To the best of our knowledge, no study has investigated factors that may explain inconsistent or diverging results concerning the effect of refactoring techniques on software quality. Therefore, in this study, scenarios of using refactoring techniques factor have been identified, investigated, and thoroughly analyzed. Ten of the most commonly used refactoring techniques in practice have been chosen and individually applied in seven case studies of varying sizes (small, medium, and large). The Quality Model for Object-Oriented Design (QMOOD) is used to assess how refactoring techniques affect quality attributes. The findings provide strong evidence that this factor plays a significant role in producing the various effects of refactoring techniques on quality attributes. These findings can help software developers understand how to use refactoring techniques to improve software quality while taking this factor into account. The best scenario for using each refactoring technique to improve software system quality has been identified. The findings can provide guidelines for software developers to use refactoring techniques to improve the quality of software systems based on the best scenarios of using the refactoring techniques.

Development of a Quality-Based Model for Software Architecture Optimization: A Case Study of Monolith and Microservice Architectures

Various architectures can be applied in software design. The aim of this research is to examine a typical implementation of Jakarta EE monolithic and microservice software architectures in the context of software quality attributes. Software quality standards are used to define quality models, as well as quality characteristics and sub-characteristics, i.e., software quality attributes. This paper evaluates monolithic and microservice architectures in the context of Coupling, Testability, Security, Complexity, Deployability, and Availability quality attributes. The performed examinations yielded a quality-based mixed integer goal programming mathematical model for software architecture optimization. The model incorporates various software metrics and considers their maximal, minimal or targeted values, as well as upper and lower deviations. The objective is the sum of all deviations, which should be minimal. Considering the presented model, a solution which incorporated multiple monoliths and microservices was defined. This way, the internal structure of the software is defined in a consistent and symmetrical context, while the external software behavior remains unchanged. In addition, an intersection point of monolithic and microservice software architectures, where software metrics obtain the same values, was introduced. Within the intersection point, either one of the architectures can be applied. With the exception of some metrics, an increase in the number of features leads to a value increase of software metrics in microservice software architecture, whilst these values are constant in monolithic software architecture. An increase in the number of features indicated a quality attribute’s importance for the software system should be examined and an appropriate architecture should be selected accordingly. Finally, practical recommendations regarding software architectures in terms of software quality were given. Since each software system needs to meet non-functional in addition to functional requirements, a quality-driven software engineering can be established.

An ensemble meta-estimator to predict source code testability

Unlike most other software quality attributes, testability cannot be evaluated solely based on the characteristics of the source code. The effectiveness of the test suite and the budget assigned to the test highly impact the testability of the code under test. The size of a test suite determines the test effort and cost, while the coverage measure indicates the test effectiveness. Therefore, testability can be measured based on the coverage and number of test cases provided by a test suite, considering the test budget. This paper offers a new equation to estimate testability regarding the size and coverage of a given test suite. The equation has been used to label 23,000 classes belonging to 110 Java projects with their testability measure. The labeled classes were vectorized using 262 metrics. The labeled vectors were fed into a family of supervised machine learning algorithms, regression, to predict testability in terms of the source code metrics. Regression models predicted testability with an R2 of 0.68 and a mean squared error of 0.03, suitable in practice. Fifteen software metrics highly affecting testability prediction were identified using a feature importance analysis technique on the learned model. The proposed models have improved mean absolute error by 38% due to utilizing new criteria, metrics, and data compared with the relevant study on predicting branch coverage as a test criterion. As an application of testability prediction, it is demonstrated that automated refactoring of 42 smelly Java classes targeted at improving the 15 influential software metrics could elevate their testability by an average of 86.87%.

Optimization of Software Quality Attributes using Evolutionary Algorithm

Software quality is a multidimensional concept. Single attribute can’t define the overall quality of the software. Software developer aims to develop software that possesses maximum software quality which depends upon various software quality attributes such as understand ability, flexibility, reusability, effectiveness, extendibility, functionality, and many more. All these software quality attributes are linked with each other and conflicting in nature. Further, these quality attributes depend upon the design properties of the software. During the designing phase of software, developers must optimize the design properties to develop good software quality. To obtain the appropriate value optimization is done. This paper implemented two multi-objective evolutionary algorithms (NSGA-2 and MOEA/D) to optimize software design properties to enhance software quality. While comparing NSGA-2 algorithm with original values it is found that there is a 1.73% improvement in the software quality on the other hand MOEA/D shows a 3.58% improvement in the software quality.

A Reference Architecture for Data-Driven and Adaptive Internet-Delivered Psychological Treatment Systems: Software Architecture Development and Validation Study.

BackgroundInternet-delivered psychological treatment (IDPT) systems are software applications that offer psychological treatments via the internet. Such IDPT systems have become one of the most commonly practiced and widely researched forms of psychotherapy. Evidence shows that psychological treatments delivered by IDPT systems can be an effective way of treating mental health morbidities. However, current IDPT systems have high dropout rates and low user adherence. The primary reason is that the current IDPT systems are not flexible, adaptable, and personalized as they follow a fixed tunnel-based treatment architecture. A fixed tunnel-based architecture follows predefined, sequential treatment content for every patient, irrespective of their context, preferences, and needs. Moreover, current IDPT systems have poor interoperability, making it difficult to reuse and share treatment materials. There is a lack of development and documentation standards, conceptual frameworks, and established (clinical) guidelines for such IDPT systems. As a result, several ad hoc forms of IDPT models exist. Consequently, developers and researchers have tended to reinvent new versions of IDPT systems, making them more complex and less interoperable.ObjectiveThis study aimed to design, develop, and evaluate a reference architecture (RA) for adaptive systems that can facilitate the design and development of adaptive, interoperable, and reusable IDPT systems.MethodsThis study was conducted in collaboration with a large interdisciplinary project entitled INTROMAT (Introducing Mental Health through Adaptive Technology), which brings together information and communications technology researchers, information and communications technology industries, health researchers, patients, clinicians, and patients’ next of kin to reach its vision. First, we investigated previous studies and state-of-the-art works based on the project’s problem domain and goals. On the basis of the findings from these investigations, we identified 2 primary gaps in current IDPT systems: lack of adaptiveness and limited interoperability. Second, we used model-driven engineering and Domain-Driven Design techniques to design, develop, and validate the RA for building adaptive, interoperable, and reusable IDPT systems to address these gaps. Third, based on the proposed RA, we implemented a prototype as the open-source software. Finally, we evaluated the RA and open-source implementation using empirical (case study) and nonempirical approaches (software architecture analysis method, expert evaluation, and software quality attributes).ResultsThis paper outlines an RA that supports flexible user modeling and the adaptive delivery of treatments. To evaluate the proposed RA, we developed an open-source software based on the proposed RA. The open-source framework aims to improve development productivity, facilitate interoperability, increase reusability, and expedite communication with domain experts.ConclusionsOur results showed that the proposed RA is flexible and capable of adapting interventions based on patients’ needs, preferences, and context. Furthermore, developers and researchers can extend the proposed RA to various health care interventions.

User Experience Dimensions for E-procurement

The use of e-procurement is needed for business transactions, especially regarding procurement activities. However, system users always demand and expect to use the system without problems. Existing studies on e-procurement do not focus on user experience (UX). Only a few studies have identified dimensions for UX evaluation; however, they are for e-government online services and construction. Identifying the UX dimensions for e-procurement is important for measuring user experience to provide better services. Therefore, this study attempted to investigate and determine the dimensions of user experience for e-procurement. The method for selecting articles was adopted from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The study analysed the data using thematic analysis based on the Systems and software Quality Requirements and Evaluation (SQuaRE) standards, such as ISO 25022:2016 and ISO 25023:2016, as guidance. The findings showed that among the most used UX dimensions in the e-procurement literature were satisfaction, security, transparency, efficiency, and reliability. Other UX-related dimensions identified from the review were usability, compatibility, effectiveness, performance efficiency, functional suitability, attractiveness, explainability, fairness, and visibility. The study was conducted to identify the UX dimensions for e-procurement from literature studies by organising them using ISO 25022:2016 and ISO 25023:2016 standards. This study could serve as a guideline for designers, developers, and researchers to develop an e-procurement system by referring to the proposed UX dimensions to produce a positive user experience. Moreover, the findings are beneficial to practitioners on software quality attributes.

Refactoring Codes to Improve Software Security Requirements

Refactoring is one of the most widely used techniques in practice to improve the quality of software, such as maintainability, testability, and understandability. However, there is a lack of studies investigating the effect of refactoring on security. The effect of refactoring on security is poorly understood and understudied. A limited number of studies provide the categorization of refactoring techniques based on their effect on quality attributes to assist developers in achieving their design objectives by selecting the most beneficial techniques and applying them at the right places with respect to specific software quality attributes. However, security was not considered in these studies. Therefore, this study aims to investigate the effect of refactoring techniques on security in terms of information hiding. The aforementioned objectives were achieved by conducting several steps starting with selecting suitable refactoring techniques, selecting five case studies, selecting security metrics, applying the refactoring techniques, and conducting multi-case analysis. Then, the chosen refactoring techniques were categorized based on their effect on security. The results of this study identify and analyze the effect of the refactoring techniques on security metrics and then propose a categorization of the refactoring techniques based on their effect on security metrics. The finding will help the developers select appropriate refactoring techniques to improve existing software security.

Towards a Formal Approach for Assessing the Design Quality of Object-Oriented Systems

The cost of software maintenance is always increasing. The companies are often confronted to failures and software errors. The quality of software to use is so required. In this paper, the authors propose a new formal approach for assessing the quality of object-oriented system design according to the quality assessment model. This approach consists in modeling the input software system by an automaton based on object-oriented design metrics and their relationship with the quality attributes. The model exhibits the importance of metrics through their links with the attributes of software quality. In addition, it is very practical and flexible for all changes. It allows the quality estimation and its validation. For the verification of proposed probabilistic model (automaton), they use the model-checking and the prism tool. The model-checking is very interesting for the evaluation and validation of the probabilistic automaton. They use it to approve the software quality of the three experimental projects. The obtained results are very interesting and of great importance.

The Correlation among Cognitive Complexity Metrics in Algorithm Analysis

In the early stage of software development, design complexity metrics are considered as useful indicators of a software testing effort and quality attributes. However, existing works made great efforts in establishing standardized metrics to evaluate the complexity of software, but there have not been significant efforts in finding the correlations among the cognitive complexity metrics. To address this challenge, this paper reviewed cognitive complexity metrics which includes: Improved Cognitive Complexity Measure (ICCM), New Cognitive Complexity of Program (NCCoP) and Modified Cognitive Complexity Measure (MCCM). The metrics were employed to analyse some selected sorting algorithms implemented in a procedural C programming language. The relationships among the aforementioned metrics were calculated using the Pearson Correlation Coefficient Method. The results of the comparative examination of ICCM, NCCoP and MCCM revealed that ICCM had more responsive measurements and that there exists a strong relationship among the specified metrics. ICCM had the strongest significance among the considered metrics based on the efforts in comprehending the information contained in the sorting algorithm codes. The study contributed significantly to understanding and addressing the complexity emanating from software development.

Estimation of software quality parameters for hybrid agile process model

This study aims to motivate a new hybrid agile methodology is a combination of agile models from Scrum, Extreme Programming, and Lean Software Development. The main aim of the hybrid agile model is the timely delivery of projects to clients with high quality at a reduced rate. But the main difficulty in hybrid agile model to effectively reflect the software quality attributes. Basically, the failure of a software project is mainly not because of inefficiency of functional features but due to inefficiency of quality attributes, like performance, reliability and effective usability. The work presents the introduction of Hybrid Agile Quality Parameter Analysis (HAQPE) that is a quality attribute driven agile development method. The outcome of developed quality attribute HAQPE was evaluated through hybrid agile process assessment by applying it to a commercial project of software industry. The results demonstrated that the developed quality attribute model is more efficient than the legacy agile development process.

Quantifying Software Quality in Agile Development Environment

Due to required efforts and the challenges involved in understanding the quantification of software quality, researchers have chosen varying quality attributes to describe the quantification of software quality. The degree of software quality is achieved from the standards and quality attributes at each development process: the adherence of software engineering principles towards realizing a product of good quality. In agile environment, the software engineering process ensures that qualities of interest are built-in and to produce software product with an acceptable level of quality. Thus, this study is aimed at quantifying six related software quality attributes. The specific objectives include identifying the software quality attributes, the design of the algorithm for measurement metrics, and to perform relational analytics of each attribute with respect to the software quality. The methodology followed an exploratory evaluation of measurement and metrics and their role in quantifying software quality in agile development environment. The study adopted existing metrics to quantify software quality attributes. Twelve opensource software projects were tested for 6 specific quality attributes and each result is quantified and presented. Results show that software number 2 (SW2) has a maintainability value of 6 minutes, 50% availability, and 0.62 reliability values. It implies that a high value of maintainability does not translate to high reliability. These values establish the relationship between attributes and enhances developers and users’ understanding of the software quality and its attributes.