Quality Of Software Development Research Articles

FIXME: synchronize with database! An empirical study of data access self-admitted technical debt

Developers sometimes choose design and implementation shortcuts due to the pressure from tight release schedules. However, shortcuts introduce technical debt that increases as the software evolves. The debt needs to be repaid as fast as possible to minimize its impact on software development and software quality. Sometimes, technical debt is admitted by developers in comments and commit messages. Such debt is known as self-admitted technical debt (SATD). In data-intensive systems, where data manipulation is a critical functionality, the presence of SATD in the data access logic could seriously harm performance and maintainability. Understanding the composition and distribution of the SATDs across software systems and their evolution could provide insights into managing technical debt efficiently. We present a large-scale empirical study on the prevalence, composition, and evolution of SATD in data-intensive systems. We analyzed 83 open-source systems relying on relational databases as well as 19 systems relying on NoSQL databases. We detected SATD in source code comments obtained from different snapshots of the subject systems. To understand the evolution dynamics of SATDs, we conducted a survival analysis. Next, we performed a manual analysis of 361 sample data-access SATDs, investigating the composition of data-access SATDs and the reasons behind their introduction and removal. We identified 15 new SATD categories, out of which 11 are specific to database access operations. We found that most of the data-access SATDs are introduced in the later stages of change history rather than at the beginning. We also observed that bug fixing and refactoring are the main reasons behind the introduction of data-access SATDs.

Systematic analysis of software development in cloud computing perceptions

AbstractCloud computing is characterized as a shared computing and communication infrastructure. It encourages the efficient and effective developmental processes that are carried out in various organizations. Cloud computing offers both possibilities and solutions of problems for outsourcing and management of software developmental operations across distinct geography. Cloud computing is adopted by organizations and application developers for developing quality software. The cloud has the significant impact on utilizing the artificial complexity required in developing and designing quality software. Software developmental organization prefers cloud computing for outsourcing tasks because of its available and scalable nature. Cloud computing is the ideal choice utilized for development modern software as they have provided a completely new way of developing real‐time cost‐effective, efficient, and quality software. Tenants (providers, developers, and consumers) are provided with platforms, software services, and infrastructure based on pay per use phenomenon. Cloud‐based software services are becoming increasingly popular, as observed by their widespread use. Cloud computing approach has drawn the interest of researchers and business because of its ability to provide a flexible and resourceful platform for development and deployment. To determine a cohesive understanding of the analyzed problems and solutions to improve the quality of software, the existing literature resources on cloud‐based software development should be analyzed and synthesized systematically. Keyword strings were formulated for analyzing relevant research articles from journals, book chapters, and conference papers. The research articles published in (2011–2021) various scientific databases were extracted and analyzed for retrieval of relevant research articles. A total of 97 research publications are examined in this SLR and are evaluated to be appropriate studies in explaining and discussing the proposed topic. The major emphasis of the presented systematic literature review (SLR) is to identify the participating entities of cloud‐based software development, challenges associated with adopting cloud for software developmental processes, and its significance to software industries and developers. This SLR will assist organizations, designers, and developers to develop and deploy user‐friendly, efficient, effective, and real time software applications.

Predictive maintenance of infrastructure code using “fluid” datasets: An exploratory study on Ansible defect proneness

AbstractThis work consolidates and compounds previous investigations in recognizing defects for infrastructure‐as‐code (IaC) scripts using general software development quality metrics with a focus on defect severity but adding to previous work an explorative look at creating datasets, which may boost the predictive power of provided models—we call this notion a fluid dataset. More specifically, we experiment with 50 different metrics harnessing a multiple dataset creation process whereby different versions of the same datasets are rigged with auto‐training facilities for model retraining and redeployment in a DataOps fashion. At this point, with a focus on the Ansible infrastructure code language—as a de facto standard for industrial‐strength infrastructure code—we build defect prediction models and manage to improve on the state of the art by finding an F1 score of 0.52 and a recall of 0.57 using a Naive–Bayes classifier. On the one hand, by improving state‐of‐the‐art defect prediction models using metrics generalizable for different IaC languages, we provide interesting leads for the future of infrastructure‐as‐code. On the other hand, we have barely scratched the surface on the novel approach of fluid‐datasets creation and automated retraining of Machine Learning (ML) defect prediction models, warranting for more research on the same direction in the future.

Empirically developed framework for building trust in distributed agile teams

Organizations are adopting agile practices in distributed software development in order to develop quality software in less time. Using agile software development in distributed set up has its own set of challenges pertaining to face to face interactions, collaboration, time zone and cultural differences. A strong presence of trust helps to overcome these challenges. A relatively lesser number of empirical studies on multidimensional perspective of trust in distributed agile software development has motivated this study.

Software Development, Configuration, Monitoring, and Management of Artificial Neural Networks

With the increasing demand for software systems, the software development industry is also developing rapidly. With the development of information technology, the more functions of the software, the more valuable it is, so the function design of the software becomes more complicated and difficult. The design of software system functions is increasingly large and complex. Scientific and effective use of software configuration management can well deal with collaborative work problems such as version management and change control in the software development process. In the process of software development and configuration, there will always be many problems that are difficult to detect. For example, when inputting the program code, there are not always some letter or space errors, and these errors are difficult to detect in time. For this reason, we need to establish a monitoring and management system for software development. As a computing model of human brain neural network, the artificial neural network can play the role of monitoring and management when it is applied to software development and configuration, which provides support for the security and scientificity of software development and configuration systems. This study studies the role and effectiveness of an artificial neural network in the monitoring and management of software development and configuration and validates it through experiments. The experimental results show that the artificial neural network has a strong ability to identify the problems in the software development configuration, which can improve the software development efficiency by at least 20%. It can improve the quality of software development and then improve the life cycle of software.

A spatial–temporal graph neural network framework for automated software bug triaging

The bug triaging process, an essential process of assigning bug reports to the most appropriate developers, is related closely to the quality and costs of software development. Since manual bug assignment is a labor-intensive task, especially for large-scale software projects, many machine learning-based approaches have been proposed to triage bug reports automatically. Although developer collaboration networks (DCNs) are dynamic and evolving in the real world, most automated bug triaging approaches focus on static tossing graphs at a single time slice. Also, none of the previous studies consider periodic interactions among developers. To address the problems mentioned above, in this article, we propose a novel spatial–temporal dynamic graph neural network (ST-DGNN) framework, including a joint random walk (JRWalk) mechanism and a graph recurrent convolutional neural network (GRCNN) model. In particular, JRWalk aims to sample topological structures in a developer collaboration network with two sampling strategies by considering both developer reputation and interaction preference. GRCNN has three components with the same structure, i.e., hourly-periodic, daily-periodic, and weekly-periodic components, to learn the spatial–temporal features of nodes on dynamic DCNs. We evaluated our approach’s effectiveness by comparing it with several state-of-the-art graph representation learning methods in three domain-specific tasks (i.e., the bug fixer prediction task and two downstream tasks of graph representation learning: node classification and link prediction). In the three tasks, experiments on two real-world, large-scale developer collaboration networks collected from the Eclipse and Mozilla projects indicate that the proposed approach outperforms all the baseline methods on three different time scales (i.e., long-term, medium-term, and short-term predictions) in terms of F1−score.

CHARACTERIZING SOFTWARE QUALITY ASSURANCE PRACTICES IN KENYA

Given the increased reliance on technology, Software Quality Assurance(SQA) has become a vital area in Software Engineering (SE). SQA practices require training, cost and often take more time than actual code writing. Owing to these requirements, software developers often ignore or partly implement SQA practices, leading to potentially poor quality software development. The goal of the study is to characterise SQA practices of software developers in Kenya. As such, quantitative empirical research was conducted. Seventy-seven (N=77) completed questionnaires were received and analysed to yield the required insights. The analysis of the findings indicates compliance with SQA practices. However, the research unearths concerns such as failure to comply with Software Development Life Cycle (SDLC) models as having the potential to lower the quality of software products. The assessment found that Unit testing was the most common type of software test. Based on the findings and literature, recommendations are made. The need to improve software engineering education and invest in software testing is underscored. The results can be generalised to most developing countries and used by software developers and trainers to identify areas in SQA that need strengthening.

MCBRank Method to Improve Software Requirements Prioritization

Software Requirements Prioritization is an important issue that has a more profound effect on the overall quality of software development. Application of software requirements prioritization provides benefits to minimize risks in software development so that the most important and most impactful requirements are given priority. This paper presents a proposed software requirements prioritization method named MCBRank, which incorporates renowned MoSCoW Method and Case-Based Ranking to improve prioritization correctness. It elaborates on the implementation of MCBRank in an empirical investigation to determine software requirements prioritization for a potential e-library system. The investigation allows the software requirements prioritization process to be implemented by using the proposed MCBRank method. A role-playing empirical investigation with 30 respondents prioritized 31 software requirements, and the results were measured by Cohen Kappa. The kappa results show that MCBRank achieves a better agreement towards the Gold Standard with kappa value of 0.60. Therefore, the investigation results support that MCBRank improves the importance of ranking correctness, representing the stakeholders’ wants and the organization's actual needs for the potential e-library system.

Queueing-Based Simulation for Software Reliability Analysis

As modern software system is growing in size and complexity, the customer expectations for software quality have become higher. In the past, many software reliability growth models (SRGMs) were proposed and they are helped to evaluate the quality of developed software. It is worth noting that some of SRGMs can be used to model the fault detection process (FDP) and the fault correction process (FCP) through an infinite server queueing (ISQ) system or a finite server queueing (FSQ) system. However, it can also be found that most ISQ and FSQ models were developed on a first come first served basis. In this paper, we propose to use the queueing-based simulations to describe the behavior of FCP and assess the software reliability instead of using model-based approaches. Our proposed queueing-based simulation techniques and simulation procedures will be able to thoroughly investigate the FCP and easily provide system performance information estimated based on the staffing level, the average response time, and the average waiting time. Numerical examples based on three real failure data are given and discussed. Our experiments show that the proposed simulation procedures obtain a good prediction capability for software reliability. We expect that the proposed methods can provide effective information for software developing management and help decision makers in resource allocation and cost control.

Documenting and implementing DevOps good practices with test automation and continuous deployment tools through software refinement

The accelerated pace of life of companies in Colombia and the world, entails the need to obtain software developments with the highest quality, in the shortest possible time and with minimal reprocessing after it is put into production. Therefore, the use of good software development practices and their automation through tools is no longer a luxury for development teams today, but part of their way of working. Unfortunately, in Colombia many of these helps and forms of work are not widely used. This paper presents the documentation and implementation of preventive quality tools and good practices for software development that allow code versioning, continuous integration, automation of functional tests, static code analysis and continuous deployment. Objective: Present the good practices implemented in the Smart Campus Ecosystem case study for software development. Methodology or method: Good practices for software development based on XP and DevOps are reviewed. A set of tools is selected for implementation that has a direct impact on the quality of software development. These tools are used in the UNIAJC Smart campus ecosystem case study. The results of the implementation are documented in this article. Results: The preventive quality model is exposed, put on test and the results are documented. Conclusions: The preventive quality model helps to increase the results of quality assurance through the set of tools that provide development teams with key information for refinement and refactoring of source code within development runtime and no later than this stage.

Optimization and practice of requirement analysis based on prototype portrait in software development process

Aiming at the problems of incomplete description, ambiguity and inconsistency in the traditional requirements analysis method based on prototype system in software development, this paper proposes a requirement analysis method based on prototype portrait, introduces the process of obtaining the software requirement specification and designing the software by using the prototype portrait method according to the user’s requirements, and formalizes the process of the method, and develops the software with the prototype portrait requirement document approved by the customer, so as to ensure the correctness of the designed functional requirements. The practical results show that the requirement analysis method based on prototype portrait overcomes the problems of long development cycle and high failure rate existing in the traditional requirements analysis method of prototype system, and improves the quality and cycle of software development.

Development of best practices in physiologically based pharmacokinetic modeling to support clinical pharmacology regulatory decision-making-A workshop summary.

Development of best practices in physiologically based pharmacokinetic modeling to support clinical pharmacology regulatory decision-making-A workshop summary.

Representation of software design using templates: impact on software quality and development effort

As a practice, software design seeks to contribute to developing quality software. During this software development stage, the requirements are translated into a representation of the software (also known as design), whose quality can be evaluated and improved. For undergraduate students, the design is difficult to understand and make. In fact, building a good design seems to require a certain level of cognitive development that few students achieve.The aim of this study is to know the effort dedicated to software detailed design and the effect on software quality when graduating students use templates to represent their design. We conducted a controlled experiment where students develop eight projects following a defined process and recording data from its execution in a software tool. We found that the use of design templates did not improve the quality of the code, measured as the defect density in the unit test phase. Also, the use of templates did not reduce the number of code smells in the analyzed code. Regarding the effort, students who use templates dedicated greater development effort to designing than to coding. Meanwhile, students who did not use templates dedicated four times less effort to designing than to coding.

Managing Contradictions in Software Engineering Investigations using the Neutrosophic IADOV Method

Software Engineering is a profession of a technological nature, which takes up theories and knowledge from various sources and addresses the development of quality software at an industrial level. Software Engineering builds knowledge around these software development practices, concretized in the definition of methods, models, and operating schemes that engineers can apply in their professional activities. However, research processes in Software Engineering lack sufficient clarity, mainly due to the level of maturity reached in this discipline. This element hinders the ability of novice researchers to design their research strategies or to recognize the research of excellence. The study is focused on evaluating the consensus and acceptance of the experts on the integration of software engineering sciences and the investigations carried out with the elements that make it up through the IADOV neutrosophic study.

Utilizing Virtual Crowd for Global Software Development

Software firms are interested in outsourcing and developing of software globally to the virtual crowd for minimizing the product cost and for increasing the software quality. Developments in information technology (IT) have changed the organizational working environment from centralized to disperse development working practices. As a result, companies have recognized the value of virtual world networks that offer benefits such as efficient time management, lower cost of growth, reduced travel costs, and access to larger competent team members to select the right skilled individual. With the wide spread of Web 3.0 applications and improvements in cloud computation technologies, multinational, multiskilled, and diverse crowds carry out the software developmental process. The aim of this research is to select the effective virtual crowd for the development of quality software. The proposed “characteristic-based virtual crowd selection (CBVCS)” method will select the crowd according to their unique characteristics such as their skills, experiences, expertise, and knowledge.

A Regression Analysis Based Model for Defect Learning and Prediction in Software Development

The development of software undergoes multiple regression phases to deliver quality software. Therefore, to minimize the development effort, time and cost it is very important to understand the probable defects associated with the designed modules. It is possible that occurrence of a range of defects may impact the designed modules which need to be predicted in advance to have a close inter-association with the depended modules. Most of the existing defect prediction classifier mechanisms are derived from the past project data learning, but it is not sufficient for new project defect predicting as the new design may have a different kind of parameters and constraints. This paper recommends Regression Analysis (RA) based defect learning and prediction Defect Prediction (RA-DP) mechanism to support the defective or non-defective prediction for quality software development. The RA-DP approach provides two methods to perform this prediction analysis. It initially presents an association learning through RA to construct the regression rules from the learned knowledge required for the defect prediction. The constructed regression rules are used for defect prediction and analysis. To measure the performance of the RA-DP a regression experimental evaluation is performed over the defect-prone PROMISE dataset from NASA project. The outcome of the results is analyzed through measuring the prediction Accuracy, Sensitivity and Specificity to demonstrate the improvisation and effectiveness of the proposal in comparison to a few existing classifiers.

The Effect of DevOps Implementation on Teamwork Quality in Software Development

Background: The Agile method, which is claimed to reduce time needed for software development cycle has been widely used. It addresses communication gaps between customers and developers. Today, the DevOps has been extended as part of the Agile process to address communication gaps between developer’s team members. Despite the rising popularity, the effect of DevOps implementation on the teamwork quality in software development is still unknown.Objective: The objective of this research is to conduct a study on the impact of DevOps on teamwork quality. Two software houses, PT X and PT Y, are chosen as the case studies.Methods: This research uses quantitative methods to analyse research data using simple linear regression. The questionnaire technique is used to retrieve respondent data using 62 questions, consisting of 20 DevOps questions from 4 indicators and 42 teamwork quality questions from 6 indicators.Results: The results from various quality tests indicate that all instruments are valid and reliable while hypothesis tests showed that the DevOps implementation variable has an influence on the teamwork quality variable by 75.6%.Conclusion: It can be concluded that the implementation of the DevOps in software development has a positive correlation with the teamwork quality.

Intelligent Collaboration Technology of Software Developers Based on Data Driven

Software development highly depends on the group contribution of developers, so improving the cooperation efficiency of developers is an important issue to improve the efficiency and quality of software development. In recent years, it has become a hot topic in the field of software engineering to improve the intelligent level of software development by mining and using the knowledge contained in software big data. However, the research on software developers and their group collaboration methods has not yet formed systematic research results. Therefore, this paper takes the developer group as the research object, through in-depth analysis of the behavior history data of developers, studies the key technologies of intelligent cooperation, and develops the corresponding support environment based on this. This paper first analyzes and summarizes the related research work. Then, the capability feature model and collaboration relationship model of software developers are given, and the knowledge map of developers is constructed. Furthermore, based on the knowledge map of developers, the collaborative development method based on intelligent recommendation is introduced by taking two developer recommendation methods as examples. Based on the above key technologies, the corresponding supporting tools are developed, and the prototype system of intelligent collaborative development environment is constructed.

A Study on the Application of Agile Methodology to Improve Software Development Quality

A Study on the Application of Agile Methodology to Improve Software Development Quality

Realities and Perspectives of Software Development in Puerto Rico: A New Analysis

Currently, within the field of Software Engineering (SE), it has been proposed to integrate four variables to develop quality software: i.) A flexible development methodology (traditional, agile, or hybrid) and adapted to the context particular to each application, ii.) a work team (trained, experienced, highly innovative, and with leadership), iii.) cutting-edge technologies, represented in the quality of the equipment and tools (automated or semi-automated) facilitating the planning, design, and manufacturing processes of products and, iv.) the inclusion of usability techniques in the process, as a guarantee to manufacture products according to customer needs. A literature search indicates that in Puerto Rico so far, there are no published articles that allow knowing the state of the art of the SE on the Island and, therefore; the organizational schemes and the type of workgroup that predominates in software development companies are unknown. Almost twenty years after the promulgation of the Agile Manifesto, in Puerto Rico, the degree of adoption of the new paradigms is still unknown. It is necessary to search for information within companies to understand what strategies are used by developers and what benefits report with their use. This new study seeks to evaluate, synthesize and present the empirical findings on software development in Puerto Rico (PR), to contrast or update the previously collected data and to know the type of applications that are carried out on the Island, what methodologies of work are the most used, how agile methods are perceived and if the approach towards usability techniques has changed in recent decades. These aspects are central to proposing to software developers an integration framework according to their needs and the demands of their clients.