High Quality Software Research Articles

Revolutionizing BA-QA Team Dynamics: AI-Driven Collaboration Platforms for Accelerated Software Quality in the US Market

In today’s fast-paced software development environment, the collaboration between Business Analysts (BAs) and Quality Assurance (QA) teams is essential for delivering high-quality products efficiently. However, traditional methods often lead to inefficiencies due to silos and misalignment between these teams. This article explores how Artificial Intelligence (AI)-driven collaboration platforms are transforming BA-QA dynamics, offering a more integrated, data-driven approach to software development. By leveraging AI technologies such as predictive analytics, automated test case generation, and real-time collaboration tools, businesses can enhance decision-making, improve communication, and optimize testing strategies. This paper discusses the key benefits of AI in accelerating software quality, highlights real-world case studies of AI applications, and examines the future potential of AI in revolutionizing BA-QA collaboration, particularly in the US market. It also addresses the emerging trends and challenges that come with adopting AI, emphasizing the importance of continuous learning, training, and integration of AI tools with other technologies like IoT and blockchain. As AI continues to evolve, its role in streamlining BA-QA collaboration will become increasingly critical, offering organizations a competitive edge in delivering high-quality software at an accelerated pace.

Pendampingan Branding, Packaging, dan Digital Marketing Tingkatkan Penjualan Madu Kelulut Desa Tuwung

Tuwung Village, located in the Kahayan Tengah District, Pulang Pisau Regency, Central Kalimantan, has significant potential in managing social forestry land to support food security. The local community, organized in the Social Forestry Business Group (KUPS), has successfully cultivated 120 hives of stingless bees (Trigona sp.), with monthly production capacity of up to 350 mL per hive. Although honey production is abundant, its sales remain very limited due to a marketing strategy that relies solely on word-of-mouth promotion. Therefore, this community service activity was introduced to provide guidance to KUPS Madu Kelulut on branding, product packaging, and digital marketing. With an attractive brand, high-quality packaging, and the right marketing strategy, it is hoped that the stingless bee honey business can become more competitive in a market already saturated with similar products. This community service program involved several stages, including preparation, implementation, and evaluation. The activity was carried out on July 9, 2024, with 40 participants. The evaluation results indicated an 81.25% increase in participants' understanding of branding, product packaging, and digital marketing. Furthermore, the participants successfully created the brand "Madu Kelulut Nyai Hapu," with an appealing packaging design and effective promotional content shared through social media and e-commerce platforms.

Analysing Quality Metrics and Automated Scoring of Code Reviews

Code reviews are an important part of the software development process, and there is a wide variety of approaches used to perform them. While it is generally agreed that code reviews are beneficial and result in higher-quality software, there has been little work investigating best practices and approaches, exploring which factors impact code review quality. Our approach firstly analyses current best practices and procedures for undertaking code reviews, along with an examination of metrics often used to analyse a review’s quality and current offerings for automated code review assessment. A maximum of one thousand code review comments per project were mined from GitHub pull requests across seven open-source projects which have previously been analysed in similar studies. Several identified metrics are tested across these projects using Python’s Natural Language Toolkit, including stop word ratio, overall sentiment, and detection of code snippets through the GitHub markdown language. Comparisons are drawn with regards to each project’s culture and the language used in the code review process, with pros and cons for each. The results show that the stop word ratio remained consistent across all projects, with only one project exceeding an average of 30%, and that the percentage of positive comments across the projects was broadly similar also. The suitability of these metrics is also discussed with regards to the creation of a scoring framework and development of an automated code review analysis tool. We conclude that the software written is an effective method of comparing practices and cultures across projects and can provide benefits by promoting a positive review culture within an organisation. However, rudimentary sentiment analysis and detection of GitHub code snippets may not be sufficient to assess a code review’s overall usefulness, as many terms that are important to include in a programmer’s lexicon such as ‘error’ and ‘fail’ deem a code review to be negative. Code snippets that are included outside of the markdown language are also ignored from analysis. Recommendations for future work are suggested, including the development of a more robust sentiment analysis system that can include detection of emotion such as frustration, and the creation of a programming dictionary to exclude programming terms from sentiment analysis.

Developing and sustaining a student-driven software solutions center—An experience report

Traditional approaches to software engineering education often limit students’ exposure and engagement to real-world projects; thereby, failing to fully harness their potential and creativity. Yet, the dynamic and rapidly-advancing digital landscape means that there is a continuous need to empower students to become active participants, problem solvers, and innovators in delivering high-quality software solutions. Therefore, it is not surprising that fresh graduates are often ill-equipped to handle industrial projects. Existing approaches to exposing students to industrial projects such as internships or capstone projects have not achieved the desired result because industries are often reluctant to assign important tasks to interns, and a capstone project is likely to be discontinued by the student after completing the course. Furthermore, all the team members in a capstone project are usually inexperienced engineers, and this may limit mentorship opportunities for the students. This paper presents an experience report on the establishment and sustenance of a student-driven software solutions center named Information Technology Solutions Center (ITSC), a unit within the School of Information Technology at the University of Cincinnati. A student-driven solution center empowers students to drive the design, development, execution, and maintenance of software solutions for industrial clients. This exposes the students to real-world projects and ensures that students are fully prepared to meet the demands of the ever-changing industrial landscape. The ITSC was established over a decade ago, has trained over 100 students, and executes about 20 projects annually with several industrial partners including Fortune 500 companies, government institutions, and research agencies. This paper discusses the establishment and maintenance of the center with the goal of motivating and providing a clear blueprint for computing programs that want to establish a similar student-driven software solutions center.

An Assessment Mechanism for Integrated Software Sustainability Evaluation Model via Evaluation Theory

Evaluation of software sustainability aids in decision-maker’s identification of the specific actions needed to guarantee sustainability for current and future generations. The prior approach to assessment focused on how the business environment was changing and using the high-quality software sustainability evaluation model (SSEM) affected those changes. Numerous well-established quality models, concepts, and understandings impacted on SSEM trends. These act as frameworks for developing software evaluations, the outcomes of which are applied to the assessment of generic software procedures. Therefore, this research aimed to use Evaluation Theory (ET) to create an assessment mechanism for an integrated Software Sustainability Evaluation Model (i-SSEM). This model encompasses evaluation criteria, targets, assessment processes, data-gathering techniques, synthesis techniques, and yardsticks. Nine criteria are presented in this study to evaluate software sustainability encompassing functional adequacy, dependability, performance efficiency, usability, security, compatibility, maintainability, portability, and impactibility. The use of the Quality Function Deployment (QFD) methodology, effectively classifies the recommended criteria into sustainable dimensions. A Goal Question Metric (GQM) is used to establish the software criteria by precisely specifying the aim, perspectives, and viewpoints of an evaluation of the sustainability aspects. By highlighting the unique evaluation mechanism for software products and processes and utilizing both quantitative and qualitative measurement techniques, this model improves the current SSEM.

Software Change Request in Software Development Project : Factors and Methods (Scoping Review Methods)

Software development is dynamic by nature, often requiring changes in software requirements through Software Change Requests (SCRs). This research aims to identify and analyze the key factors that influence SCRs in software development projects and evaluate the methods used to effectively manage these changes. This research uses the Scoping Review Method to systematically review and synthesize existing literature on SCRs, aiming to provide a comprehensive understanding of current practices, challenges, and strategies for managing software change requests. The review includes an extensive search of academic databases to identify relevant studies that address SCR factors and management methods. Key factors that influence SCR include project size, complexity, stakeholder involvement, and clarity of initial requirements. Methods for managing SCR are categorized into formal approaches, such as structured change control processes and tools, and informal approaches, which rely on ad-hoc communication and collaboration. The results underscore the importance of a well-defined change management process to mitigate the risks associated with SCR. Effective SCR management can improve project outcomes by improving software quality, increasing stakeholder satisfaction, and minimizing project delays. This research has practical implications for software development professionals by providing insight into effective SCR management techniques. This research also identifies gaps in the literature, recommending future research on the impact of agile methodologies and the use of automated tools in facilitating change management. Understanding these factors and methods enables practitioners to better navigate the complexities of software change, ensuring successful project delivery and maintaining high software quality.

Research on the "Dual System" Talent Cultivation Model of School-Enterprise Cooperation

Taking the packaging planning and design major as the research object, this paper focuses on the application of the "dual system" talent cultivation model of school-enterprise cooperation. By deeply analyzing relevant educational theories and extensively collecting and studying practical cases at home and abroad, the purpose is to comprehensively and deeply reveal the significant advantages of this cultivation model in improving the practical ability of packaging major students and enhancing their adaptability to the rapidly changing market demands. On this basis, this research further discusses the challenges and problems faced by this model in the implementation process from multiple dimensions, such as the depth and breadth of school-enterprise cooperation, the rationality of curriculum setting, and the effectiveness of practical teaching. At the same time, combined with the actual situation and development trends, a series of practical optimization strategies and constructive suggestions are put forward in a targeted manner to promote the more perfect and efficient operation of the "dual system" talent cultivation model in the packaging major and provide strong support and guarantee for cultivating high-quality packaging planning and design talents.

Enhancing Novice Developer Efficacy through UX Journey: Integrating User Experience and User Requirement to Develop Developer Skills

User experience and user requirements are two different approaches to software development. User requirements focus on meeting customer expectations and demands for software solutions, while user experience covers all aspects of software interaction with users. To increase the value of the software, the software must have usable and easy-to-use features with an attractive design or work environment that fits the user's behavior. Integrating software requirements and user experience can increase developer productivity by focusing on features that meet user requirements and expectations. This integration can also increase software development efficiency by addressing issues arising during development. This article addresses developers' challenges when addressing user needs and provides practical solutions widely accepted in industry and academia. Combining user experience and user needs into the UX Journey approach can increase developer productivity and confidence in software development. The design of the UX Journey is carried out by evaluating several existing design solution methods such as Design Thinking, IDEO, HPI, and Double Diamond to determine the existing conditions and needs for the problems faced. Then, by mapping the user, context, and domain, the model is obtained. appropriate. The proposed model comprises Discover, Explore, Test, and Listen activities. A trial was carried out on the respondents to test the method, and a feasibility test and an implementation schedule were obtained based on the statistical analysis of the initial user. It took 980-1500 minutes to complete the design solution. Focusing on features that align with user needs and improve problem-solving efficiency throughout development gives developers greater confidence in producing high-quality software.

CIPerf: a benchmark for continuous integration services performance and cost analysis

Continuous Integration is a crucial practice in modern software development, enabling teams to automate the process of building, testing, and merging code increments to ensure continuous delivery of high-quality software. Despite its growing adoption, the cost and performance of Continuous Integration services often go unexamined in sufficient detail. This paper presents CIPerf, a comprehensive benchmark designed to analyze both the performance and cost of cloud-based and self-hosted Continuous Integration services. The study centers on a comparison between two specific services: Bitbucket Pipelines, a cloud-based offering by Atlassian, and Hetzner, a self-hosted solution. By focusing on these platforms, the research aims to provide practical insights into the real-world costs and execution performance of Continuous Integration services. To achieve this, CIPerf conducted automated tests on an hourly basis over a two-month period, measuring critical timeframes such as resource provisioning, environment setup, and the actual test execution times. The results showed significant differences in both the cost efficiency and the consistency of performance between the two services. For instance, Bitbucket Pipelines, while convenient in its cloud-based offering, demonstrated higher variability in provisioning times compared to the stable, predictable performance of Hetzner’s self-hosted environment. The analysis also explored how these performance metrics influence key software development metrics, including deployment frequency and developer productivity. CIPerf provides a clear methodology for developers and organizations to objectively assess their Continuous Integration service options, ultimately helping them optimize their workflows. Moreover, this benchmark can serve as an ongoing tool to monitor service performance over time, identifying potential degradations or improvements in service quality, thus offering longterm value for teams that rely on Continuous Integration for their development processes.

An ontological knowledge-based method for handling feature model defects due to dead feature

The specifications of a certain domain are addressed by a portfolio of software products, known as Software Product Line (SPL). Feature Model (FM) supports domain engineering by modeling domain knowledge along with variability among SPL. The quality of FM is one of the significant factors for the successful SPL in order to attain high quality software products. However, the benefits of SPL can be reduced due to defects in FM. Dead Feature (DF) is one of such defects. Several approaches exist in the literature to detect defects due to DF in FMs. But only a few can handle their sources and solutions which are cumbersome and difficult to understand by humans. An ontological knowledge-based method for handling defects due to DF in FMs is described in this paper. It specifies FM in the form of ontology-based knowledge representation. The rules based on first-order logic are created and implemented using Prolog to detect defects due to DF with sources as well as suggest solutions to resolve these defects. A case study of the product line available on SPLOT repository is utilized for illustrating the proposed work. The experiments are performed with real-world FMs of varied sizes from SPLOT and FMs created with the FeatureIDE tool. The results prove the efficiency, scalability (up to model with 32,000 features) and accuracy of the presented method. Therefore, reusability of DFs free knowledge enables deriving defect free products from SPL and eventually enhances the quality of SPL.

Research on software reliability enhancement methods based on iterative development

Iterative development is an agile software development methodology that speeds up the development process and increases the flexibility of software delivery. However, the current software requirements change extremely fast, which leads to its increasing uncertainty, and iterative development also faces the challenge of software reliability. The software reliability enhancement method based on iterative development is researched and discussed for the current situation of insufficient reliability in the traditional software development process. In traditional software development, one-time delivery pressure and overall design complexity often lead to software functional defects and instability, affecting software reliability. The iterative development method, on the other hand, improves software reliability by decomposing the software development process into multiple iterative cycles, each of which contains requirements analysis, design, coding, and testing, updating the software functionality in smaller increments, and gradually improving the software product. This study proposes a software reliability improvement methodology by exploring and analyzing the key factors related to reliability in the iterative development process. This dissertation can establish a reliable and high-quality software development process through the steps of meticulous requirements planning and management, iterative development and testing, automated testing and continuous integration, code quality control, defect management and continuous improvement, user feedback and user engagement, and continuous learning and technology enhancement.

Information and Communication Tools for Effective Functioning of Distributed Project Teams

The paper analyzes the key aspects of the use of Agile methodologies by distributed project teams in the IT industry with an emphasis on the integration of information and communication systems for effective work in dynamic environments. The challenges faced by the virtual IT industry in the context of the COVID pandemic, the large-scale invasion of Russia, which generated radical changes in the approaches to the management of project teams, have been considered. The considerable attention is paid to the role of video conferencing and other digital tools, which have become basic in ensuring uninterrupted work and effective interaction in distributed project teams, during global crises. An analysis of the main stack of information and communication technologies in distributed project environments indicates the widespread use of such platforms as Zoom, Microsoft Teams, Google Meet, Skype, Slack, Trello, Jira, and others to ensure flexibility, adaptability and high productivity of teams. The importance of synergy between the Agile methodology and its derivatives and digital tools is highlighted, demonstrating how it helps distributed IT project teams to adapt to changes, ensure high quality software development and foster effective communities of like-minded people. The paper provides information on the use of Agile methodology in 2023, which indicates the growth in the number of distributed project teams and captures the key factors for their successful functioning. It focuses on the importance of the integration processes of modern information and communication technologies and adaptive management practices in the dynamic world of IT projects, making a valuable contribution to the understanding of how distributed project teams can maintain a high level of productivity and efficiency, overcoming systemic obstacles and challenges that are formed in poorly predicted project environments.

Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model

Software defect prediction aims to find a reliable method for predicting defects in a particular software project and assisting software engineers in allocating limited resources to release high-quality software products. While most earlier research has concentrated on employing traditional features, current methodologies are increasingly directed toward extracting semantic features from source code. Traditional features often fall short in identifying semantic differences within programs, differences that are essential for the development of reliable and effective prediction models. In contrast, semantic features cannot present statistical metrics about the source code, such as the code size and complexity. Thus, using only one kind of feature negatively affects prediction performance. To bridge the gap between the traditional and semantic features, we propose a novel defect prediction model that integrates traditional and semantic features using a hybrid deep learning approach to address this limitation. Specifically, our model employs a hybrid CNN-MLP classifier: the convolutional neural network (CNN) processes semantic features extracted from projects’ abstract syntax trees (ASTs) using Word2vec. In contrast, the traditional features extracted from the dataset repository are processed by a multilayer perceptron (MLP). Outputs of CNN and MLP are then integrated and fed into a fully connected layer for defect prediction. Extensive experiments are conducted on various open-source projects to validate CNN-MLP’s effectiveness. Experimental results indicate that CNN-MLP can significantly enhance defect prediction performance. Furthermore, CNN-MLP’s improvements outperform existing methods in non-effort-aware and effort-aware cases.

Teleworking and its relationship with workplace technostress post COVID-19

The overwhelming use of information and communications technologies (ICT) that is being implemented in organizations makes it necessary to analyze the effects of teleworking on employees. The objective of this review was to analyze the scientific literature published on the relationship between teleworking and work-related technostress in the post-COVID-19 context. The method used had a qualitative approach, carrying out a systematic review that respected the PRISMA guidelines. 20 empirical investigations found in Scopus and Google Scholar were analyzed, published between 2019 and 2023. The results categorically indicate that there is a close relationship between teleworking and work-related technostress. The factors are social isolation, complex software, conflict between work and the personal environment, the absence of interpersonal relationships, and technological uncertainty. Therefore, it is suggested that organizations promote work autonomy and effective self-leadership, in addition to considering measures such as training in the use of digital platforms, the implementation of high-quality software that allows better interaction, and the organization of periodic face-to-face activities.

Bug Tracker User Guide

ABSTRACT___ The Bug Tracking Tool project addresses the pressing need for efficient bug tracking and management in contemporary software development. By leveraging modern web technologies and agile methodologies, the project aims to develop a comprehensive solution that streamlines bug identification, tracking, and resolution processes. Through a literature survey, key observations and shortcomings in existing bug tracking tools were identified, guiding the proposed work. The project’s methodology encompasses requirements gathering, design, implementation, testing, and deployment, focusing on user-centric design and functionality. Preliminary results demonstrate progress towards achieving project objectives, highlighting the tool’s potential to enhance software quality assurance practices and user satisfaction. Despite encountering challenges such as technical complexity and resource constraints, the project lays a solid foundation for future enhancements and optimizations. Overall, the Bug Tracking Tool project represents a significant step towards improving bug tracking practices and delivering higher-quality software products to end-users INDEX TEARMS – HTML, CSS , JavaScript, Python, Django Framework, SQLlite.

Integrating Heuristic Evaluation and Think-Aloud Protocols By Applying Nielsen’s Metrics on Indonesian e-Pangan Application

Usability testing has emerged as one of the most important aspects of software testing to provide high-quality software to users. The purpose of this study is to evaluate the usability of an Indonesian mobile government application known as e-pangan. Because of the growing number of mobile phone users, the government has deployed mobile applications to make it easier for people to obtain information. Unfortunately, mobile government is not user-friendly and still has a lot of issues, as evidenced by user reviews in the Google Play store. The study employed a hybrid usability methodology that included heuristic evaluation and think-aloud protocols. The mobile government application Panel Harga Pangan will be tested to represent the study of this testing. Furthermore, the heuristic evaluation is performed by a usability expert, while the think-aloud protocols are performed by ten users. Nielsen's metrics are applied to measure the learnability, efficiency, memorability, error rate, and satisfaction of the application. The results of the research show that the application has an average score of 66%, indicating that while some users might discover it challenging others might discover it to be straightforward. This research is expected to be beneficial for usability practitioners or developers to develop a user-friendly e-government application.

Pengamatan Testing Sistem Informasi Pada Website DemoBlaze

This project aims to test a dummy e-commerce website to ensure it meets the requirements and functions properly as expected. The testing is conducted using the Python programming language with IntelliJ IDEA as the Integrated Development Environment, and utilizing a tool called Selenium to test the website with Chrome WebDriver as the connector to execute commands on the Google Chrome browser. The testing process includes simulating various user activities such as registration, login, adding items to the cart on the website, and online payment transactions. Each testing step is designed to identify and fix potential issues, ensuring that all main website functions run smoothly and meet user expectations. Furthermore, this testing aims to evaluate students' ability to write effective testing scripts. By conducting this testing, students are expected to gain valuable practical experience in software testing and enhance their understanding of the importance of testing in the software development cycle. The results of this testing are expected to enhance students' understanding of the importance of testing in the software development cycle and ensure that the e-commerce site can provide an optimal user experience. Overall, this project emphasizes the importance of comprehensive and structured testing to seek high-quality software.

Problem Faced During the Software Development Cycle

The software development cycle cover a series of stages , each compelling to the successful design of software products. Though each stage illustrates eccentric challenges that can block progress and impact the final product’s quality. This paper examines the common problems faced during the software development cycle , including issues in requirements gathering , design , implementation , testing , and maintenance. Through a precise analysis of these stages , we recognize basic interference such as unclear requirements , scope creep , inappropriate structure , coding errors , integration challenges , insufficient testing ,and technical liability. The paper also proposes methods to ease these concern , aiming to enhance the efficiency and effectiveness of software development processes. By understanding and addressing these problems , software development teams can enhance the project outcomes and deliver higher-quality software products. Keywords— Software development , requirements gathering , software design , coding errors , integration challenges , software testing , scope creep , inappropriate structure , technical liability , software cycle .

Integration of UML Class Diagrams Based on Semantics and Structure

As a high-level reuse, software design reuse has received more and more attention because of its important impact on the subsequent development stages. Usually, the design models are typically represented as some graphs or diagrams, in which Unified Modeling Language (UML) class diagram is so widely used in software design that it has become the de facto standard. There has been some research on the reuse of UML class diagrams so far, mainly focusing on matching and retrieval. However, it is worth noting that there are many similar class diagrams modeling the same object or some related class diagrams modeling different aspects of the same object in the reuse repository. As a matter of fact, the primary step to achieve a high-quality reuse is to have high-quality software artifacts, so the well-designed UML class diagrams become a necessary resource for software design reuse. Therefore, it is necessary to integrate these class diagrams so that they have stronger modeling ability, and eliminating redundancy is another benefit of the integration. Up to now, there has been little discussion about the integration of class diagrams, so we propose an integration approach based on semantics and structure in this paper. The equivalent elements that can identify the semantically merged parts are defined, and the possible conflict items are listed from both semantic and structural aspects. The integration procedure composed of three stages is proposed, in which an approach combining semantic common class diagrams (SCCDs) and structural common graph sequence (SCGS) is combined to determine the merged parts, the integration issue of heterogeneous class diagrams is considered from the proposed abstract models, and the conflict resolution for each conflict item is described by examples. The experimental results show the effectiveness of our proposed integration approach.

Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction

Machine learning-based software defect prediction (SDP) approaches have been commonly proposed to help to deliver high-quality software. Unfortunately, all the previous research conducted without effective feature reduction suffers from high-dimensional data, leading to unsatisfactory prediction performance measures. Moreover, without proper feature reduction, the interpretability and generalization ability of machine learning models in SDP may be compromised, hindering their practical utility in diverse software development environments. In this paper, an SDP approach using deep Q-learning network (DQN)-based feature extraction is proposed to eliminate irrelevant, redundant, and noisy features and improve the classification performance. In the data preprocessing phase, the undersampling method of BalanceCascade is applied to divide the original datasets. As the first step of feature extraction, the weight ranking of all the metric elements is calculated according to the expected cross-entropy. Then, the relation matrix is constructed by applying random matrix theory. After that, the reward principle is defined for computing the Q value of Q-learning based on weight ranking, relation matrix, and the number of errors, according to which a convolutional neural network model is trained on datasets until the sequences of metric pairs are generated for all datasets acting as the revised feature set. Various experiments have been conducted on 11 NASA and 11 PROMISE repository datasets. Sensitive analysis experiments show that binary classification algorithms based on SDP approaches using the DQN-based feature extraction outperform those without using it. We also conducted experiments to compare our approach with four state-of-the-art approaches on common datasets, which show that our approach is superior to these methods in precision, F-measure, area under receiver operating characteristics curve, and Matthews correlation coefficient values.