Software Product Quality Research Articles

Software quality model based on SQuaRE standards

Quality is one of the factors that ensure the commercial success and safety of using the software. Quality is understood as conformity the explicit and implicit requirements of various stakeholders. It is necessary to ensure a joint understanding between developers and users, engineers need to understand the meaning of the concept of quality, characteristics and importance of quality for the developed or maintained software. Measurements are the basis for quality assurance. They are the main tool for managing the life cycle of software products, assessing the implementation of plans and monitoring. To quantify quality, it is necessary to measure the characteristics of the software. Standardization provides unification of requirements for quality, its measurement and assessment. The use of standards has many potential benefits for any organization, especially in key areas such as measuring the quality of software products, information and measurement systems. Recognized international standards organizations have published the ISO/IEC 25000 series of standards for systems and software quality requirements and evaluation SQuaRE, which is gaining widespread practical application. The paper discusses a series of international standards SQuaRE, analyzes the relationship between the quality model, quality characteristics, quality measures and a new concept – a quality measure element of the software, presents the measurement of quality based on these standards.

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study

Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation.

Komparasi Algoritma Logistic Regression dan Random Forest pada Prediksi Cacat Software

Testing becomes the standard in producing quality software, testing can be assessed through certain measures and methods, one of the benchmarks for software quality is ISO, which was made by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) , In the prediction of software defects software defect prediction error is a very bad thing, the prediction results can have an effect on the software itself. This study compares the results of the Logistic Regression Algorithm and Random Forest before and after the resampling method is applied, the test results show that Random Forest with resampling produces a higher level of accuracy. From the test results above, it can be concluded that the Random Forest with resampling method is more effective in predicting software defects

Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality

Execution tracing is a tool used in the course of software development and software maintenance to identify the internal routes of execution and state changes while the software operates. Its quality has a high influence on the duration of the analysis required to locate software faults. Nevertheless, execution tracing quality has not been described by a quality model, which is an impediment while measuring software product quality. In addition, such a model needs to consider uncertainty, as the underlying factors involve human analysis and assessment. The goal of this study is to address both issues and to fill the gap by defining a quality model for execution tracing. The data collection was conducted on a defined study population with the inclusion of software professionals to consider their accumulated experiences; moreover, the data were processed by genetic algorithms to identify the linguistic rules of a fuzzy inference system. The linguistic rules constitute a human-interpretable rule set that offers further insights into the problem domain. The study found that the quality properties accuracy, design and implementation have the strongest impact on the quality of execution tracing, while the property legibility is necessary but not completely inevitable. Furthermore, the quality property security shows adverse effects on the quality of execution tracing, but its presence is required to some extent to avoid leaking information and to satisfy legal expectations. The created model is able to describe execution tracing quality appropriately. In future work, the researchers plan to link the constructed quality model to overall software product quality frameworks to consider execution tracing quality with regard to software product quality as a whole. In addition, the simplification of the mathematically complex model is also planned to ensure an easy-to-tailor approach to specific application domains.

Information support of nature management and environmental protection in Ukraine

This research develops the problem of effectiveness of the management system and protection of the natural environment. In this research, the various factors of management activity are proposed. To achieve this goal we conducted research on information and analytical systems, which are implemented in the management of nature and environmental protection of Ukraine at three levels: regional (oblast), district and urban. The article presents the results of the analysis of classifiers for systematization of circulating documents, functions of computer document management systems, including those implemented in the management of nature and environmental protection, as well as the level of satisfaction with the quality of software products used to management. It is established that for information support of management activities the most necessary are databases on financial and economic activities, regulatory, personnel, resource, logistics and environmental statistics. The importance of information flow system analysis and information technologies are constantly developing and adapting to user awareness. In the investigation underlined the practical use of different methods to solve similar problems taking into account the peculiarities of the organization.

Privacy preserving defect prediction using generalization and entropy-based data reduction

The software engineering community produces data that can be analyzed to enhance the quality of future software products, and data regarding software defects can be used by data scientists to create defect predictors. However, sharing such data raises privacy concerns, since sensitive software features are usually considered as business assets that should be protected in accordance with the law. Early research efforts on protecting the privacy of software data found that applying conventional data anonymization to mask sensitive attributes of software features degrades the quality of the shared data. In addition, data produced by such approaches is not immune to attacks such as inference and background knowledge attacks. This research proposes a new approach to share protected release of software defects data that can still be used in data science algorithms. We created a generalization (clustering)-based approach to anonymize sensitive software attributes. Tomek link and AllNN data reduction approaches were used to discard noisy records that may affect the usefulness of the shared data. The proposed approach considers diversity of sensitive attributes as an important factor to avoid inference and background knowledge attacks on the anonymized data, therefore data discarded is removed from both defective and non-defective records. We conducted experiments conducted on several benchmark software defect datasets, using both data quality and privacy measures to evaluate the proposed approach. Our findings showed that the proposed approach outperforms existing well-known techniques using accuracy and privacy measures.

On quality assessment of electronic training course interfaces

The development of e-learning, both in distance and mixed forms, becomes especially relevant in the modern educational process. A high-quality e-learning course is developed through the efforts of two parties: the teacher, who creates the methodological content, and the programmer, who creates the electronic shell of the course. The article substantiates the importance of quality issues in the development of a user interface for electronic educational resources, since the user of an electronic course deals with the direct implementation of educational material. The indicators for assessing the quality of software products in accordance with international and Russian standards and their applicability for assessing user interfaces of electronic educational resources are analyzed. The conclusion is made about the importance of the indicator “practicality” in relation to this type of software product as an indicator of an individual evaluation of the use of a product by a certain user or circle of users. The classical methods for assessing the quality of the human-machine interaction interface and the applicability of experimental and formal methods for assessing quality are considered. The analysis of modern approaches to the design of user interfaces based on UX/UI design is given. An assessment of the requirements and criteria for assessing the user interface from the standpoint of modern design is given. The tasks and features of the UX and UI components of the design process are analyzed. The essence of the modern term “usability” as an indicator of the interface evaluation is explained, and the qualitative evaluation criteria for this indicator are considered. The concept of UX testing is given, the main stages of this process are considered. The importance of taking into account the subjective psychological factors of interface perception is substantiated. The indicators for assessing the quality of user interfaces, based on the cognitive factors of its perception by a person, are analyzed.

Measurement of software quality based on international standards

Quality is one of the factors that ensure the commercial success and safety of using the software. Quality is understood as conformity the explicit and implicit requirements of various stakeholders. It is necessary to ensure a joint understanding between developers and users, engineers need to understand the meaning of the concept of quality, characteristics and importance of quality for the developed or maintained software. Measurements are the basis for quality assurance. They are the main tool for managing the life cycle of software products, assessing the implementation of plans and monitoring. To quantify quality, it is necessary to measure the characteristics of the software. Standardization provides unification of requirements for quality, its measurement and assessment. The use of standards has many potential benefits for any organization, especially in key areas such as measuring the quality of software products, information and measurement systems. Recognized international standards organizations have published the ISO/IEC 25000 series of standards for systems and software quality requirements and evaluation (SQuaRE), which is gaining widespread practical application. The paper discusses a series of the SQuaRE international standards, analyzes the relationship between the quality model, quality characteristics, quality measures and a new concept, i.e., a quality measure element of the software, presents the measurement of quality based on these standards.

Metaheuristics and Software Engineering: Past, Present, and Future

This work aims at giving an updated vision on the successful combination between Metaheuristics and Software Engineering (SE). Mostly during the 90s, varied groups of researchers dealing with search, optimization, and learning (SOL) met SE researchers, all of them looking for a quantified manner of modeling and solving problems in the software field. This paper will discuss on the construction, assessment, and exploitation tasks that help in making software programs a scientific object, subject to automatic study and control. We also want to show with several case studies how the quantification of software features and the automatic search for bugs can improve the software quality process, which eases compliance to ISO/IEEE standards. In short, we want to build intelligent automatic tools that will upgrade the quality of software products and services. Since we approach this new field as a cross-fertilization between two research domains, we then need to talk not only on metaheuristics for SE (well known by now), but also on SE for metaheuristics (not so well known nowadays). In summary, we will discuss here with three time horizons in mind: the old times [before the term search-based SE (SBSE) was used for this], the recent years on SBSE, and the many avenues for future research/development. A new body of knowledge in SOL and SE exists internationally, which is resulting in a new class of researchers able of building intelligent techniques for the benefit of software, that is, of modern societies.

IBSI Education “SIWESI” Quality Measurement Based on System Technical Viewpoint

The need for information systems is increasing every year. The information system of an organization or company is useful for managing data to produce useful and effective information that supports activities and all levels of management that use it. The aspect of software quality is one of the important things in the development of a software. To determine the quality of software products, a measurement is needed. IBSI Education, one of which is a Tutoring Institution that uses “SiWeSi” to support administration, view schedules, see student progress, and so on. The success of the software that is built is based on whether or not the software works according to the objectives to be achieved. In the scientific concept of software engineering, the success of software is not only seen from the suitability of the resulting product to existing needs. The success of the software is also seen from the software development process. This study uses the McCall quality model because this method fulfills many components of the assessment and looks at quality from the user's point of view. The quality model used to measure the quality of SIWESI is determined from the results of the literature study.

On the Use of Quality Models to Address Distinct Quality Views

Different software product quality models interpret different amounts of information, i.e., they can capture and address different manifestations of software quality. This characteristic can cause misleading statements and misunderstandings while explaining or comparing the results of software product quality assessments. A total of 23 previously identified distinct software product quality models are analysed on how they handle the abstract notion of quality, and a taxonomy on the quality manifestations that the individual software product quality models are able to capture is established. Quality models that are able to solely describe the quality manifestation of the source code are attractive due to their full automation potential through static code analysers, but their assessment results ignore a huge part of software product quality, which is the one that most impresses the end user. The manifestations of software product quality that address the behaviour of the software while it operates, or the perception of the end user with regard to the software in use, require human involvement in the quality assessment. The taxonomy contributes to interpreting the quality assessment results of different quality models by showing the possible quality manifestations that can be captured by the identified models; moreover, the taxonomy also provides assistance while selecting a quality model for a given project. The quality manifestations used for the quality measurement always need to be presented, otherwise the quality assessment results cannot be interpreted in an appropriate manner.

Software defect prediction with imbalanced distribution by radius‐synthetic minority over‐sampling technique

AbstractSoftware defect prediction, which can identify the defect‐prone modules, is an effective technology to ensure the quality of software products. Due to the importance in software maintenance, many learning‐based software defect prediction models are presented in recent years. Actually, the defects usually occupy a very small proportions in software source codes; thus, the imbalanced distributions between defect‐prone modules and non‐defect‐prone modules increase the learning difficulty of the classification task. To address this issue, we present a random over‐sampling mechanism used to generate minority‐class samples from high‐dimensional sampling space to deal with the imbalanced distributions in software defect prediction, in which two constraints are applied to provide a robust way to generate new synthetic samples, that is, scaling the random over‐sampling scope to a reasonable area and distinguishing the majority‐class samples in a critical region. Based on nine open datasets of software projects, we experimentally verify that our presented method is effective on predict the defect‐prone modules, and the effect is superior to the traditional imbalanced processing methods.

Hellinger Net: A Hybrid Imbalance Learning Model to Improve Software Defect Prediction

Software defect prediction (SDP) is a convenient way to identify defects in the early phases of the software development life cycle. This early warning system can help in the removal of software defects and yield a cost-effective and good quality of software products. A wide range of statistical and machine learning models have been employed to predict defects in software modules. But the imbalanced nature of this type of SDP datasets is pivotal for the successful development of a defect prediction model. Imbalanced software datasets contain nonuniform class distributions with a few instances belonging to a specific class compared to that of the other class. This article proposes a novel hybrid methodology, namely the Hellinger net model, for imbalanced learning to improve defect prediction for software modules. Hellinger net, a tree to network mapped model, is a deep feedforward neural network with a built-in hierarchy, just like decision trees. Hellinger net also utilizes the strength of a skew insensitive distance measure, namely Hellinger distance, in handling class imbalance problems. On the theoretical side, this article proves the theoretical consistency of the proposed model. A thorough experiment was conducted over ten NASA SDP datasets to show the superiority of the proposed method.

Multicriteria Decision Making to Continuous Software Improvement Based on Quality Management, Assurance, and Metrics

Software process improvement methodologies support to incessantly regulate and advance the process of software for its performance development. This development arises in terms of software product quality, time, changes reduction, and so on. The aim of software process improvement is to make software process effective and to increase quality of software product through continuous evaluation. Various approaches and frameworks are in practice for software process improvement. Early decision making concerning continuous software improvement can ultimately lead to successful software developments and growing software industry and business of software. Keeping in view the early decision making regarding continuous software improvement, the proposed study has considered a decision support system (DSS) for making decision based on multicriteria against quality management, assurance, and metrics. The necessary factors and alternatives for continuous software improvement were considered. The process of the proposed study was experimentally conducted, and results were good enough for supporting the study. The study will help practitioners in early decision making regarding software improvement based on quality management.

Genetic Algorithm for Test Suite Optimization: An Experimental Investigation of Different Selection Methods

Software Testing is an important aspect of the real time software development process. Software testing always assures the quality of software product. As associated with software testing, there are few very important issues where there is a need to pay attention on it in the process of software development test. These issues are generation of effective test case and test suite as well as optimization of test case and suite while doing testing of software product. The important issue is that testing time of the test case and test suite. It is very much important that after development of software product effective testing should be performed. So to overcome these issues of optimization, we have proposed new approach for test suite optimization using genetic algorithm (GA). Genetic algorithm is evolutionary in nature so it is often used for optimization of problem by researcher. In this paper, our aim is to study various selections methods like tournament selection, rank selection and roulette wheel selection and then we apply this genetic algorithm (GA) on various programs which will generate optimized test suite with parameters like fitness value of test case, test suite and take minimum amount of time for execution after certain preset generation. In this paper our main objectives as per the experimental investigation, we show that tournament selection works very fine as compared to other methods with respect fitness selection of test case and test suites, testing time of test case and test suites as well as number of requirements.

Ensemble Machine Learning Model for Software Defect Prediction

Software defect prediction is a significant activity in every software firm. It helps in producing quality software by reliable defect prediction, defect elimination, and prediction of modules that are susceptible to defect. Several researchers have proposed different software prediction approaches in the past. However, these conventional software defect predictions are prone to low classification accuracy, time-consuming, and tasking. This paper aims to develop a novel multi-model ensemble machine-learning for software defect prediction. The ensemble technique can reduce inconsistency among training and test datasets and eliminate bias in the training and testing phase of the model, thereby overcoming the downsides that have characterized the existing techniques used for the prediction of a software defect. To address these shortcomings, this paper proposes a new ensemble machine-learning model for software defect prediction using k Nearest Neighbour (kNN), Generalized Linear Model with Elastic Net Regularization (GLMNet), and Linear Discriminant Analysis (LDA) with Random Forest as base learner. Experiments were conducted using the proposed model on CM1, JM1, KC3, and PC3 datasets from the NASA PROMISE repository using the RStudio simulation tool. The ensemble technique achieved 87.69% for CM1 dataset, 81.11% for JM1 dataset, 90.70% for PC3 dataset, and 94.74% for KC3 dataset. The performance of the proposed system was compared with that of other existing techniques in literature in terms of AUC. The ensemble technique achieved 87%, which is better than the other seven state-of-the-art techniques under consideration. On average, the proposed model achieved an overall prediction accuracy of 88.56% for all datasets used for experiments. The results demonstrated that the ensemble model succeeded in effectively predicting the defects in PROMISE datasets that are notorious for their noisy features and high dimensions. This shows that ensemble machine learning is promising and the future of software defect prediction.

A family of standards for software and systems product lines

There is a drastic rise and evolution of Software and Systems Product Lines (SSPL) as a potential solution to deliver quality software products in a shorter time and with less effort. This has led to a plethora of diversified tools across the entire life cycle of SSPL, making it difficult for product line organizations to select the right set of tools for product line realization and tool vendors to develop interoperable tools. In this paper, we motivate the need for standardization of tools and methods for SSPL. The ISO/IEC JTC1/SC7 (Software and Systems Engineering) of the International Organization for Standardization (ISO) has proposed a set of interrelated standards in the area of SSPL to cater to the emerging demands of software and systems industry. We present the foundation of SSPL family of standards that are driven by market needs and discuss the core patterns and processes of SSPL. We then present an overview of ISO/IEC 26550, which is the reference model for SSPL family of standards and provide examples from current and ongoing standards. We highlight a few examples of industry adoption of SSPL and standards and emphasize the need for further research for standards in SSPL. We see this paper as a first step to bring standards knowledge into the academic world in the field of SSPL.

Studying test-driven development and its retainment over a six-month time span

In this paper, we investigate the effect of TDD, as compared to a non-TDD approach, as well as its retainment (or retention) over a time span of (about) six months. To pursue these objectives, we conducted a (quantitative) longitudinal cohort study with 30 novice developers (i.e., third-year undergraduate students in Computer Science). We observed that TDD affects neither the external quality of software products nor developers’ productivity. However, we observed that the participants applying TDD produced significantly more tests, with a higher fault-detection capability, than those using a non-TDD approach. As for the retainment of TDD, we found that TDD is retained by novice developers for at least six months.

Measuring the Quality of the Development Process Academic System with E-GQM Method

In a software development project, aspects of software quality are fundamental; all stakeholders expect high-quality software. To ensure the quality of software products, it is necessary to ensure the software quality process. A software process is essential to be assessed from their quality. In the software development process, the developer needs guidance in carrying out every aspect of it. The goals to achieve and the procedure to measure for each aspect's goals performance must be determined. One method that can be used is the Extended Goal Question Metric method. This method determines what aspects must be achieved for each development process. A few goals to measure are defined for each aspect. For each goal, one or more goals determine one or more relevant questions. For each question, an appropriate metric is  determined. The next step is mapping between G to Q and Q to M. The measurement was conducted by calculating the goal value obtained from the metric calculation. From this metric, each goal's value could be obtained, whether it is achieved or not. The tests were carried out on the software process to develop the academic Directorate of Technology and Information System Development of Institut Teknologi Sepuluh Nopember Surabaya, Indonesia. Each goal's value exceeded 0.51 (for a scale of 0-1), which achieved the Software development process's quality. The total average score was 0.889.