Software Quality Assessment Research Articles

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.

A Hierarchical Model for Quality Evaluation of Mixed Source Software Based on ISO/IEC 25010

With the emergence of mixed source software, the existing quality models are not able to better assess the community quality and autonomy controllability of mixed source software. To fill this gap, we propose a hierarchical model in this paper for quality assessment of mixed source software. In our model, the new attributes are proposed to meet the quality requirements of mixed source software based on the ISO/IEC 25010 standards, and a set of metrics are designed for the new attributes. The model evaluates the quality of mixed source software through quality attributes that have been quantified by the metrics. Applying our quality model to some mixed source software and comparing the model results with the actual situation, we verify whether our proposed two quality attributes, community intensity and autonomy controllability, can effectively assess the quality of mixed source software. The results of the experiments show that our model is indeed effective in assessing the quality of mixed source software. An important feature of our model is that the model has good flexibility, and the set of quality attributes and metrics can be adjusted freely, which provides a flexible and feasible way for various software quality assessment requirements.

Neural Network-Based Model for the Quality Assessment of Object-Oriented Software

Software quality assessment is an important subject among the researchers in the software development domain. The quality assessment is generally done either at the design level through some of the design attributes or through code when the product is ready. These two types of software quality are referred to as design quality and product quality, respectively. Several techniques and tools are available that facilitate to assess the design as well as the product quality of software. In this paper, a neural network model is proposed for the assessment of quality of object-oriented software at the product level. The authors select a subset of existing object-oriented metrics that are normalized at three levels and used to find quality factors like understandability, reusability, flexibility, maintainability, reliability, extensibility, and modifiability for the model development. The model is validated by assessing quality levels of 33 open source object-oriented software of different design complexities and observing a high correlation between these quality levels in comparison with an existing model.

Homogenous Multiple Classifier System for Software Quality Assessment Based on Support Vector Machine

In today's society, almost all human endeavours depend on software products. Lack of quality software is one of the software industry's most important problems. Hence, it would be beneficial to access the quality of software to improve and enhance software products while increasing customer satisfaction. This paper assesses software product quality using a Support Vector Machine-based ensemble classifier. The ISO/IEC-9126 (International Organization for Standardization 2001) software quality (SQ) framework was adopted in this work. Dimension reduction of the product metric category dataset and the entire PM dataset was conducted using linear discriminant analysis (LDA). SVM kernel functions (linear, quadratic, cubic, fine gaussian, medium gaussian and coarse gaussian) were used to model each classifier. The combinations of the results from the multiple SVMs used AdaBoost, bagging, and random subspace ensemble methods for the assessment of SQ. All three ensemble learning methods performed better than the individual SVM, however, the bagging stood out with an accuracy of 93.0%. Hence, it was adopted in the fusion of the SVM results and classification of SQ into classes. Results from the confusion matrix and receivers’ operating characteristics were greater than 97.99% and confirm significant improvements with an ensemble of homogenous classifiers based on SVM.

J’ai dû me renseigner sur la DSAC en effectuant des recherches sur Google

<h3>Abstract</h3> Software plays a crucial and growing role in research. Unfortunately, the computational component in Life Sciences research is challenging to reproduce and verify most of the time. It could be undocumented, opaque, may even contain unknown errors that affect the outcome, or be directly unavailable, and impossible to use by others. These issues are detrimental to the overall quality of scientific research. One step to address this problem is the formulation of principles that research software in the domain should meet to ensure its quality and sustainability, resembling the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. Within the ELIXIR infrastructure, OpenEBench aims to be an open platform providing both support for scientific benchmarking and an active observatory of software quality for the universe of Life Sciences research software. We present our initial proposal to instantiate a FAIR-like framework for assessing software quality as the first step toward the implementation of such an observatory in OpenEBench.

Software System Selection for Open University System

Software quality assessment is one of the key factors for the success of universities, so this study focuses on the study of technical quality. Measurement factors apply to universities in information systems, including software quality. The novel model is used to test whether the implementation of e-learning system in higher education institutions will succeed or fail. The scope of this investigation is free and the evaluated universities have open source e-learning systems that measure and evaluate quality according to our proposed software quality model. Educational Information System (AIS), which combines many software quality standards to help system analysts, system developers, and systems. Programmers in their AIS projects. Quality model, six quality characteristics are shown as the minimum requirements for creating a new model. This has been further enhanced by the expansion of the standard approach to measuring the quality of AIS based on a combination of several. Software quality standards used in these educational systems with new features. Using the novel model to test whether e-learning is implemented, the system will succeed or fail in higher education institutions. Our contribution and development will be discussed on this. The new model aims to guide educational institutions that are in the process of creating their e-learning system to evaluate and select the right software. Features that are essential for the success of the entire system. In addition, this paper provides several reviews of current methods used to select AIS components and outlines their disadvantages in terms of our advanced models.

SOFTWARE QUALITY METRIC ANALYSIS

The analysis of software quality metrics showed that there are no common standards for choosing metrics, so each developer of a testing system must independently propose their own methods for assessing software quality, taking into account the characteristics of the industry in which the software product will operate.

Translating quality-driven code change selection to an instance of multiple-criteria decision making

The definition and assessment of software quality have not converged to a single specification. Each team may formulate its own notion of quality and tools and methodologies for measuring it. Software quality can be improved via code changes, most often as part of a software maintenance loop. This manuscript contributes towards providing decision support for code change selection given a) a set of preferences on a software product’s qualities and b) a pool of heterogeneous code changes to select from. We formulate the problem as an instance of Multiple-Criteria Decision Making, for which we provide both an abstract flavor and a prototype implementation. Our prototype targets energy efficiency, technical debt and dependability. This prototype achieved inconsistent results, in the sense of not always recommending changes reflecting the decision maker’s preferences. Encouraged from some positive cases and cognizant of our prototype’s shortcomings, we propose directions for future research. This paper should thus be viewed as an imperfect first step towards quality-driven, code change-centered decision support and, simultaneously, as a curious yet pragmatic enough gaze on the road ahead.

Software Defect Prediction and Software Quality Assessment Using Dlr-Lvq and Fuzzy Rules

Recently, Software development has been considerably grown. Fault in the software causes fault and interrupts the output. Characteristics like these make it much challenging to avert software flaws. Spontaneously forecasting the amount of flaws within the software modules is essential and also can assist developers to proficiently allot restricted resources. Recently, numerous Software Defect Prediction (SDP) techniques are developed. But, the accuracy and time consuming challenges still remain to be solved. Also, a few top-notch techniques don't properly classify the software whereas it is a needed metric to ensure quality standards. This work proffers a novel Decaying Learning Rate – Learning vector Quantization (DLR-LVQ) classifier to forecast the software defect. The proposed methods consist of the following steps: redundant data removal, feature extraction (FE), feature oversampling, data normalization, defect prediction (DP), and quality prediction. The proposed DLR-LVQ’s attained outcome is assessed with the existent methodologies. The outcomes exhibit that the methodology proposed attains efficient classification outcomes are examined. Keywords: Software Defect Prediction (SDP), Non defective software quality prediction, BM-SMOTE, Decaying Learning Rate, Learning Vector Quantization, Fuzzy rules, HDFS and Map Reduce.

Software Requirements Profile Quality Model

Article opens series of works devoted to profile-oriented software quality assessment. In this article the concept of software requirements profiling for subsequent software requirements profile quality assessment is analyzed and developed. The main result of the article is the development of the software requirements profile quality model. The model describes the following: characteristics and attributes of software requirements and their classification features; characteristics and attributes of software requirements profile and their classification features; semantics and syntax of software requirements. The article is based on analysis and use of the following standards: ISO/IEC 25012:2008, ISO/IEC/IEEE 29148:2018, ISO/IEC/IEEE 29148:2011. Examples of software requirements profile quality model are introduced. The suggested approach is used for the development of terms of reference or draft of a new standard. For example, the model is used for an assessment draft of the new standard “Requirements to computer security of NPP Instrumentation and Control Systems (NPP I&amp;C)” developed by the Ukrainian state regulatory body. As a result of the development of the software requirements profile quality model, a set of propositions for improving the quality of the standard as a branch of the profile for NPP I&amp;C cybersecurity are implemented.

Evaluation of the International Monitoring System and International Data Centre of the Comprehensive Nuclear-Test-Ban Treaty Organization

Abstract Evaluation and quality assurance activities of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) are reviewed with special emphasis on radionuclide technologies. The CTBTO carries out detailed evaluation in all fields of technical verification of the Treaty. The goal is to provide States Signatories with confidence in the quality of data from the International Monitoring System and data products of the International Data Centre. The largest technical evaluation effort has been the quality assessment of the operational software. About 1.3 million lines of source code and scripts were checked. Software characteristics, such as maintainability, were assessed using automated tool-based techniques and improvements were suggested. Specific to radionuclide technologies, several methods have been developed to cope with the large amounts of spectra produced each day by 80 radionuclide monitoring stations around the world. Some of the key evaluation results, such as the peak detection capability of the operational software are presented in detail.

GreenMicro: Identifying Microservices From Use Cases in Greenfield Development

Microservices architecture is a new paradigm for developing a software system as a collection of independent services that communicate through lightweight protocols. In greenfield development, identifying the microservices is not a trivial task, as there is no legacy code lying around and no old development to start with. Thus, identification of microservices from requirements becomes an important decision during the analysis and design phase. Use cases play a vital role in the requirements analysis modeling phases in a model-driven software engineering process. It is a technique of capturing high-level user functions and scope of the system. In this paper, we propose GreenMicro, an automatic microservice identification technique that utilizes the use cases model and the database entities. Both features are the artifacts of analysis and design phase that depict complete functionality of an overall system. In essence, a collection of related use cases indicates a bounded context of the system that can be grouped in a suitable way as microservices. Therefore, our approach GreenMicro clusters close-knit use cases to recover meaningful microservices. We investigate and validate our approach on an in-house proprietary web application and three sample benchmark applications. We have mapped our approach to state-of-the-art software quality assessment attributes and have presented the results. Preliminary results are motivating and the proposed methodology works as anticipated in producing functionally cohesive and loosely coupled microservice candidate recommendations. Our approach enables the system architects to identify microservice candidates at an early analysis and design phase of development.

Software Quality Evaluation by Cocomo II With NN and SVM

Cost, time and quality projection are the crucial aspects in software development process. Incorrect estimations can cause losses which in turn may lead to irreversible damage. It is generally perceived that a imperfectly estimated project always results in a substandard quality due to the efforts being wrongly directed. Firstly Effort Estimation is calculated by actual effort and proposed Effort. That Effort evaluation of 500 NASA projects, after that evaluation is done by four parameters Standard Error, Standard Deviation, Mean Absolute Error, Root Mean Square Error. The author amalgamated the robustness of COCOMO-II with that of Neural Network NN and Support Vector Machine SVM .Quality Which we evaluate that is quality Evaluation of Semantic Web Application. In the last checks the majority of all four parameters for software quality assessment.

Qualimetry Essentials Applied to Embedded Software Development

ABSTRACTWith the initial goal of filling gaps in the definition, assessment, control, and prediction of automotive embedded software quality, including standards and regulatory compliance constraints, a three‐year research study was conducted. The multiple contributions of the study to theoretical and applied qualimetry have helped to define a theoretical framework for quality control and monitoring of engineering and product development processes. Then, demonstrating the practicality of this solution, this study was applied on a real example from the automotive industry. This framework initially developed in the scope of embedded software is also applicable to systems and embedded systems, and makes these contributions have an impact on all industrial sectors.

A Review of Software Components Identification Methods and Quality Assessment Criteria

Identification of suitable reusable software components from repositories and other sources is a key process in software reuse.&nbsp; Many methods for components identifying exist.&nbsp; However, considering the increased adoption of reuse in software projects and the need to identify appropriate components from the numerous sources based on multiple quality criteria, it becomes imperative to undertake a review of existing methods to determine their strengths and weaknesses as well as gaps in components identification. &nbsp;In view of the above, this research was designed to undertake a systematic review of components identification methods in software reuse.&nbsp; Research findings indicate that existing methods and automated tools were inadequate in handling components identification from multiple sources especially where multiple quality criteria are considered.&nbsp; Also, findings indicate that multi-objective optimization algorithms like non-dominated sorting genetic algorithm III (NSGA-III) are capable of addressing such multidimensional problems; therefore, greater efforts in designing and developing automated tools that are based on this technique are highly recommended.&nbsp; The need for adequate methods for cataloguing and categorizing components that are stored in repositories and other sources is also identified as capable of enhancing the process of components identification in software reuse.

Methodological foundation for improving the quality of intelligent decision-making system software

On the basis of a detailed analysis, existing terminological interpretations of the concept of "software quality" have been generalized, conclusions are drawn about the correspondence of the terms used to assess the quality of general software in the process of assessing the quality of software of intelligent decision-making systems (IDMS). It has been proved that the quality of the IDMS software is a complex multi-criteria indicator that takes into account not only the performance of the individual software module as a subsystem, but also the causal relationships of the elements of the software system itself. The main differences in software quality assessment between the functional and formal approaches are shown. The structure of the criterion of guarantor capacity of decision-making systems software has been investigated and conclusions have been drawn on the influence of its main components on the evaluation of IDMS software and on the provision of reliable computing process. On the basis of the analysis of the list of attributes and the quality metric of the IDMS software, it is established that the guarantee is determined by the reliability of the software structure itself and is characterised by the restoration of the functional state after failures or failures. The interrelationship and influence of IDMS software design quality indicators on the characteristics and sub-characteristics of the IDMS software is established, an example of the interrelationship between characteristics (factors) and quality indicators, the method of measuring quality indicators and design processes is given. On the basis of the conducted research, IDMS software denial regimes have been defined and their impact on the decision-making process has been shown. Detailed classes of failures and their influence on compliance of IDMS software with the task of development are shown. It has been shown that the reliability of IDMS is a dynamic concept, manifested in time, and is strongly dependent on the presence / absence of defects in the interaction. A detailed analysis of methods of software quality assurance and control has been carried out, and conclusions have been drawn on the possibility of their application IDMS software. The maturity model of the IDMS software has been improved and validated, and the maturity structure of the software as an indicator of the quality of the IDMS has been introduced.

Research and Analysis of Software Defect Measurement Elements Based on Analytic Hierarchy Process

It is not convenient for the general software quality evaluation system to be directly and quickly applied to the software quality evaluation to be tested. Therefore, this paper proposes a method suitable for the analysis of factors affecting software quality based on analytic hierarchy process. The analytic hierarchy process is used as the main framework, the distribution of the software’s own properties and accumulated test findings as input,the constructed defect weight ratio is used as the judgment matrix, Then do the hierarchical analysis. Through the analytic hierarchy process to analyse the weight of the factors of the software itself, test organizations can quickly make a fuzzy assessment of software quality, and provides the reference basis for the reasonable allocation of test resources.

METHODOLOGICAL APPROACHES TO SOFTWARE QUALITY ASSESSMENT FOR OIL AND GAS COMPLEX OBJECTS

This article establishes methodological approaches to software quality assessment in the process of information technology implementation in oil and gas companies. This study offers methodological basis of software quality assessment drawing on complexanalyses of&#x0D; methodological approaches to software quality assessment conducted by the author/&#x0D; The article investigates and analyses the most commonly used approaches to software quality assessment for information systems.&#x0D; The study examines methodological approaches to software quality assessment for oil and gas complex objects.&#x0D; The article describes current state of affairs and the results of previous research in this field including the main methods of software quality assessment.&#x0D; This research describes and analyses some of the problems connected to creation and implementation of software code quality assessment methods in general and oil and gas industry knowledge base in particular.&#x0D; The article presents arguments for necessity for assessment of software quality expenses.

Statical and Dynamical Software Analysis

The development of software built with quality has become an important trend and a natural choice in many organisations. Currently, methods of measurement and assessment of software quality, security, trustworthiness cannot guarantee safe and reliable operations of software systems completely and effectively.In this article statistical and dynamical software analysis methods, main concepts and techniques families are overviewed.The article has an overview of why combination of several analysis techniques is necessary for software quality and examples how static and dynamical analysis may be introduced in a modern agile software development life cycle.As a summary of techniques for software analysis, represented on Table 1, due to the computability barrier, no technique can provide fully automatic, robust, and complete analyses. Testing sacrifices robustness. Assisted proving is not automatic (even if it is often partly automated, the main proof arguments generally need to be human provided). Model-checking approaches can achieve robustness and completeness only with respect to finite models, and they generally give up completeness when considering programs (the incompleteness is often introduced in the modeling stage). Static analysis gives up completeness (though it may be designed to be precise for large classes of interested programs). Last, bug finding is neither robust nor complete. Another important dimension is scalability. In practice, all approaches have limitations regarding scalability, although these limitations vary depending on the intended applications (e.g., input programs, target properties, and algorithms used).Already implemented code could be analysed in a continuous integration environment by a tool like SonarQube. Properly configured metrics and quality gates provide fast and detailed feedback on incremental changes starting from development machine till highload enterprise production environments. Software analysis helps to improve quality and development speed in Agile development life cycle with reasonable cost.Manuscript received 16.05.2020