Aspects Of Software Development Research Articles

Enhancing software engineering practices with generative AI: A framework for automated code synthesis and refactoring

This paper is based on how software development has been revolutionized using AI in automation, mainly dealing with code synthesis and rewrite frameworks. While there is no focused definition for software development with AI technologies, their application in development processes is unlikely to remain marginal as they mature and provide higher productivity, improved code quality, and enhanced ability for automating repetitive tasks in development. Automating coding means that predefined tools can bring code suggestions, show and apply refactoring features, and enforce coding standards so developers can devote their efforts to other aspects of software development. The following paper describes the practical systems that should be adopted before AI can be incorporated into software development. It also covers essential factors such as ethical, security, and quality aspects regarding the proper use of artificial intelligence. Lastly, the paper focuses on the direction and innovations in the future, including domain-specific AI models, better explainability of the AI solutions, and collaborative tools that can work according to improved and modified development practices. The framework is intended to balance machine learning with human experience to help developers utilize the benefits that an AI-Based Software Development Framework can render.

AI's Impact on Traditional Software Development

This paper explores the transformative impact of artificial intelligence (AI) on traditional software development practices. As AI technologies advance, they are reshaping various aspects of software development, including design, coding, testing, and maintenance. This paper examines how AI tools and techniques challenge conventional development methodologies, evaluates the benefits and limitations of AI integration, and discusses the implications for development teams and the industry at large. Through analysis and case studies, the paper provides insights into how AI is altering the landscape of software development.

The Role of Machine Learning in Software Development

In today's rapidly evolving technological era, the role of machine learning in software development has become increasingly vital and influential. Machine learning has revolutionized various aspects of software development, from code analysis and optimization to prediction and decision-making. Moreover, machine learning algorithms have the potential to significantly enhance the software development process by automating repetitive tasks, improving code quality, and reducing the time and effort required for software testing and debugging. By gaining lots of data and powerful computing resources, machine learning algorithms can be able to analyse patterns and make accurate predictions about software performance, identify potential bugs or security issues, and assist in improving software design and development processes. Furthermore, machine learning can facilitate software maintenance and debugging by detecting anomalies and identifying potential causes of software failures. Albeit, using machine learning techniques into the software development process can greatly improve efficiency, productivity, and overall software quality. In this paper it is presenting the tools, techniques and the application of Machine Learning (ML) in different phases of Software Development Life Cycle (SDLC) for enhancing and improving the software development process.

Unit Test Generation Using Large Language Models: A Systematic Literature Review

Unit Test Generation Using Large Language Models: A Systematic Literature Review

Software Bug Prediction Using Machine Learning

Software bug prediction is a crucial aspect of software development and maintenance as it directly impacts the overall success of the software. By identifying potential bugs early on, software quality, reliability, and efficiency can be improved while also helping to reduce costs. Creating a reliable bug prediction model is a complex task, with various techniques proposed in the literature. In this paper, a bug prediction model utilizing machine learning algorithms is introduced. Three supervised machine learning algorithms have been incorporated to forecast future software faults using historical data. The evaluation process revealed that Machine Learning algorithms, specifically Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Networks (ANNs), can be effectively utilized with a high level of accuracy. Additionally, a comparison measure was implemented to assess the proposed prediction model against other methods. The findings indicated that the Machine Learning approach outperformed other techniques in terms of performance. Key Words: Bug Prediction, Reliability, Efficiency, Quality, Machine Learning, Performance Metrics

BRIDGING THEORY AND PRACTICE: COMPARATIVE INSIGHTS INTO EVALUATING USER INTERFACES

Abstract Keywords Cite This Article as User interface (UI) evaluation is an important aspect of software development, ensuring that software or systems are efficient and user satisfactory. Among the various usability testing methods available, Cognitive Walkthrough Method, Heuristic Evaluation Method and Concurrent Usability Testing Method stand out as most popular approaches. This research paper presents a comparative analysis of these testing methods, emphasizing their strengths, flaws, and applicability in different situations. Through a detailed analysis of each testing method's procedural steps, and practical concerns, we aim to provide insights into their effectiveness and suitability for evaluating User Interface (UI) designs. Additionally, we discuss potential synergies and inadequacies when combining these testing methods in practice. Our results and findings contribute to a deeper understanding of UI evaluation techniques and provide guidance for selecting the most appropriate approach for testing team’s specific needs.

Software Effort Estimation using Machine Learning Algorithms

Effort estimation is a crucial aspect of software development, as it helps project managers plan, control, and schedule the development of software systems. This research study compares various machine learning techniques for estimating effort in software development, focusing on the most widely used and recent methods. The paper begins by highlighting the significance of effort estimation and its associated difficulties. It then presents a comprehensive overview of the different categories of effort estimation techniques, including algorithmic, model-based, and expert-based methods. The study concludes by comparing methods for a given software development project. Random Forest Regression algorithm performs well on the given dataset tested along with various Regression algorithms, including Support Vector, Linear, and Decision Tree Regression. Additionally, the research identifies areas for future investigation in software effort estimation, including the requirement for more accurate and reliable methods and the need to address the inherent complexity and uncertainty in software development projects. This paper provides a comprehensive examination of the current state-of-the-art in software effort estimation, serving as a resource for researchers in the field of software engineering.

A Survey on Factors Preventing the Adoption of Automated Software Testing: A Principal Component Analysis Approach

Automated software testing is a crucial yet resource-intensive aspect of software development. This burden on resources affects widespread adoption, with expertise and cost being the primary challenges preventing adoption. This paper focuses on automated testing driven by manually created test cases, acknowledging its advantages while critically analysing its implications across various development stages that are affecting its adoption. Additionally, it analyses the differences in perception between those in nontechnical and technical roles, where nontechnical roles (e.g., management) predominantly strive to reduce costs and delivery time, whereas technical roles are often driven by quality and completeness. This study investigates the difference in attitudes toward automated testing (AtAT), specifically focusing on why it is not adopted. This article presents a survey conducted among software industry professionals that spans various roles to determine common trends and draw conclusions. A two-stage approach is presented, comprising a comprehensive descriptive analysis and the use of Principal Component Analysis. In total, 81 participants received a series of 22 questions, and their responses were compared against job role types and experience levels. In summary, six key findings are presented that cover expertise, time, cost, tools and techniques, utilisation, organisation, and capacity.

DEVELOPMENT AND IMPLEMENTATION OF A SYSTEM OF METRICS FOR EVALUATING THE QUALITY OF SOFTWARE PRODUCTS

This article discusses the development and implementation of a system of metrics for assigning employees of various levels of training to projects of varying complexity. Quality is an important aspect of software development, therefore, the development of a system that allows you to evaluate and control the assignment of employees to projects at various stages of creating a software product is an urgent task. The article suggests an approach to the development of such a system based on the analysis of existing methods and practices. The system of metrics for assessing competencies and complexity of projects may include various indicators, such as the number of integrations of a project, the complexity of the code, or experience in implementing complex integration projects or experience in implementing similar projects. The implementation of the system will allow you to evaluate the project and assign to it a certain training of analysts, testers and developers at various stages of software product development, which will allow you to implement it competently and without a large number of errors, as well as reduce development time. In addition, the article discusses the methodology of using a system of metrics for assigning human resources. This includes analyzing the data obtained, identifying trends and errors, as well as taking appropriate measures to assign resources to projects. Using the system will allow development companies to make more informed decisions regarding the quality of software products and increase customer satisfaction. In general, the article presents the basic principles and methods of developing and implementing a system for implementing a control system and assigning employees to projects, as well as describes the practical applications and benefits of using such a system.

EXAMINING SOFTWARE QUALITY CONCEPT: BUSINESS ANALYSIS PERSPECTIVE

Software quality is a critical aspect of software development that significantly impacts business performance and customer satisfaction. However, defining software quality can be challenging, as different sources provide various definitions and perspectives. The article presents a literature review of software quality, acknowledging an ongoing debate over the years regarding the definition of software quality and the methods used for its assessment. Among all the different ideas about software quality, the article highlights key concepts that are crucial in understanding software quality: meeting requirements, satisfying users, using software features, and spotting defects. The article also checks out international standards like ISO/IEC 25010:2011 and ISO/IEC 5055:2021, introducing terms such as "Quality in use" and "Structural Quality." Unveiling a tripartite perspective elucidated in international standards—internal quality, external quality, and quality in use - the article underscores the intricate interplay between subjectivity and objectivity. The subjective dimension, influenced by user perception and contextual factors, is juxtaposed with more objective criteria such as conformance to requirements and the absence of defects. The standards provide helpful perspectives, but the human side of things, like user feelings and specific contexts, makes finding a universal definition tricky. The pivotal role of business analysis and requirements engineering in ensuring software quality is underscored. Business requirements, stakeholder needs, and the quality of functional and non-functional requirements emerge as integral components. The article argues that software quality is intricately tied to the quality of its requirements, presenting a dual perspective: compliance with quality criteria and alignment with stakeholders' expectations and business goals. Practical software quality assessment is built upon the foundational understanding of contextual nuances, user needs, and operational conditions, all discerned through business analysis.

A Mobile-Based Used Vehicle Buying System Implementation

Aspect-oriented software is characterized by enhanced reusability and maintainability compared to object-oriented software. In aspect-oriented software development, concerns scattered across objects are separated and modularized into aspects. This research is motivated by the buyer's mistrust in used vehicle transactions due to concerns over potential fraud. To address this, the study proposes designing an e-commerce application that facilitates interactions between prospective vehicle sellers and buyers, alleviating transactional apprehensions. The analysis and design phases of the software development cycle employ Use Cases to facilitate the identification and separation of concerns. Use Cases are chosen for their direct illustration of user-system interactions. The case study focuses on a mobile e-commerce application for used vehicle sellers and buyers. The research aims to analyze the impact of consumer motivation, perceived quality, and consumer attitudes on the decision to purchase used vehicles. The findings are expected to aid sellers in marketing their vehicles and assist buyers in making informed purchasing decisions

Deep Learning for Software Defect Prediction: An LSTM-based Approach

Software defect prediction is an important aspect of software development, as it helps developers and organizations to identify and resolve bugs in the software before they become major issues. In this paper, we explore the use of machine learning algorithms for software defect prediction. We discuss the different types of machine learning algorithms that have been used for software defect prediction and their advantages and disadvantages. We also provide a comprehensive review of recent studies that have used machine learning algorithms for software defect prediction. The paper concludes with a discussion of the challenges and opportunities in using machine learning algorithms for software defect prediction and the future directions of research in this field. This paper surveys the existing literature on software defect prediction, focusing specifically on deep learning techniques. Compared to existing surveys on the topic, this paper offers a more in-depth analysis of the strengths and weaknesses of deep learning approaches for software defect prediction. It explores the use of LSTMs for this task, which have not been extensively studied in previous surveys. Additionally, this paper provides a comprehensive review of recent research in the field, highlighting the most promising deep learning models and techniques for software defect prediction. The results of this survey demonstrate that LSTM-based deep learning models can outperform traditional machine learning approaches and achieve state-of-the-art results in software defect prediction. Furthermore, this paper provides insights into the challenges and limitations of deep learning approaches for software defect prediction, highlighting areas for future research and improvement. Overall, this paper offers a valuable resource for researchers and practitioners interested in using deep learning techniques for software defect prediction..

Deep Learning for Software Defect Prediction: An LSTM-based Approach

Software defect prediction is an important aspect of software development, as it helps developers and organizations to identify and resolve bugs in the software before they become major issues. In this paper, we explore the use of machine learning algorithms for software defect prediction. We discuss the different types of machine learning algorithms that have been used for software defect prediction and their advantages and disadvantages. We also provide a comprehensive review of recent studies that have used machine learning algorithms for software defect prediction. The paper concludes with a discussion of the challenges and opportunities in using machine learning algorithms for software defect prediction and the future directions of research in this field. This paper surveys the existing literature on software defect prediction, focusing specifically on deep learning techniques. Compared to existing surveys on the topic, this paper offers a more in-depth analysis of the strengths and weaknesses of deep learning approaches for software defect prediction. It explores the use of LSTMs for this task, which have not been extensively studied in previous surveys. Additionally, this paper provides a comprehensive review of recent research in the field, highlighting the most promising deep learning models and techniques for software defect prediction. The results of this survey demonstrate that LSTM-based deep learning models can outperform traditional machine learning approaches and achieve state-of-the-art results in software defect prediction. Furthermore, this paper provides insights into the challenges and limitations of deep learning approaches for software defect prediction, highlighting areas for future research and improvement. Overall, this paper offers a valuable resource for researchers and practitioners interested in using deep learning techniques for software defect prediction..

PARTn: A plugin implementation of the Activation Relaxation Technique nouveau that takes over the FIRE minimisation algorithm

Nowadays, the interoperability and interfacing of codes and libraries have become crucial aspects of software development and engineering, and the basis for enabling and simplifying the sharing of methods and tools, both within and among communities. One of the most important bottlenecks that arises when developing and maintaining an interface of a library with an already existing software, is to keep it aligned with the development route of the latter. This might include significant changes, such as changes in the data structures used by the library, which are communicated through the interface.In this paper, an approach for inserting a new algorithm into existing software is presented, through a minimally invasive interface, that takes over an already present algorithm, and thus changes its original purpose. The approach is applied to the well-established Activation-Relaxation Technique nouveau (ARTn) algorithm, that is revisited and re-engineered to bias and take over the FIRE minimization algorithm, as presently implemented in two community codes for atomistic simulations, namely Quantum ESPRESSO (PWscf) and LAMMPS. ARTn is a well established single-ended saddle-point search algorithm that allows for the exploration of potential energy surfaces. The resulting algorithm acts as a plugin, and is distributed in the form of an external library (pARTn). Program summaryProgram Title: plugin Activation Relaxation Technique nouveau (pARTn)CPC Library link to program files:https://doi.org/10.17632/xpwr56d2yk.1Developer's repository link:https://gitlab.com/mammasmias/artn-pluginLicensing provisions: Apache-2.0/GPLv3Programming language: Modern Fortran and C++Nature of problem: Original ARTn implementation was difficult to interface, port and maintain.Solution method: Full refactoring and re-engineering into a library, which utilizes and biases a minimization algorithm already implemented in the engine, to make it behave as ARTn. The biasing is done by reverse-engineering specific external conditions that drive the displacement of atoms.

Summary of the 2nd Natural Language-based Software Engineering Workshop (NLBSE 2023)

Natural language processing (NLP) involves the automated anal- ysis and manipulation of human language. This includes algo- rithms that can analyze text created by humans and algorithms that can generate text that appears natural. Nowadays, NLP methods are becoming increasingly prevalent to enhance various aspects of software development. Indeed, throughout the software development lifecycle, numerous natural language artifacts are produced. Therefore, the existence of NLP-based approaches and tools has opened up possibilities for improving the e ectiveness and productivity of software engineers, processes, and products. The second edition of the Natural Language-Based Software Engi- neering Workshop (NLBSE) took place in 2023 alongside the 45th International Conference on Software Engineering (ICSE 2023), where the research community engaged in discussions about these approaches. This event brought together researchers and practi- tioners from the elds of NLP and software engineering to ex- change experiences, establish future research directions, and pro- mote the adoption of NLP techniques and tools in tackling chal- lenges speci c to software engineering. In this paper, we present a summary of the 2nd edition of the workshop, which comprised three full papers, four short/position papers, ve tool competi- tion/demonstration papers, two keynote talks (\Automated Bug Management: Re ections & the Road Ahead" by David Lo and \Trends and Opportunities in the Application of Large Language Models: the Quest for Maximum E ect" by Albert Ziegler), fol- lowed by extensive discussion among NLBSE participants. More details can be found at https://nlbse2023.github.io/index. html

Building Resilient Systems: Leveraging AI/ML within DevSecOps Frameworks

This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques within DevSecOps frameworks to enhance system resilience. In today's dynamic and rapidly evolving technological landscape, resilience has become a critical aspect of software development and operations. DevSecOps, an evolution of the DevOps methodology, emphasizes the importance of integrating security practices throughout the software development lifecycle. By leveraging AI/ML capabilities within DevSecOps frameworks, organizations can proactively identify and mitigate security threats, optimize system performance, and enhance overall resilience. This paper discusses various strategies for incorporating AI/ML algorithms into DevSecOps workflows, including anomaly detection, predictive analytics, and automated incident response. Furthermore, it examines the challenges and considerations associated with implementing AI/ML-driven approaches within DevSecOps environments, such as data privacy concerns, model interpretability, and algorithmic biases. Through a comprehensive exploration of these concepts, this paper provides insights into building resilient systems by harnessing the power of AI/ML within DevSecOps frameworks.

Qualitative software engineering research: Reflections and guidelines

AbstractResearchers are increasingly recognizing the importance of human aspects in software development. Because qualitative methods are used to explore human behavior in‐depth, we believe that studies using such methods will become more common. Existing qualitative software engineering guidelines do not cover the full breadth of qualitative methods and the knowledge on how to use them like in social sciences. The purpose of this study was to extend the software engineering community's current body of knowledge regarding available qualitative methods and their quality assurance frameworks and to provide recommendations and guidelines for their use. With the support of an epistemological argument and a survey of the literature, we suggest that future research would benefit from (1) utilizing a broader set of research methods, (2) more strongly emphasizing reflexivity, and (3) employing qualitative guidelines and quality criteria. We present an overview of three qualitative methods commonly used in social sciences but rarely seen in software engineering research, namely interpretative phenomenological analysis, narrative analysis, and discourse analysis. Furthermore, we discuss the meaning of reflexivity in relation to the software engineering context and suggest means of fostering it. Our paper will help software engineering researchers better select and then guide the application of a broader set of qualitative research methods.

What Do Transgender Software Professionals Say About a Career in the Software Industry?

The new remote work structures resulting from the post-pandemic are producing several positive outcomes in the software industry, including improving diversity in software teams. Diversity is an essential aspect of software development because technology influences almost every aspect of modern society; therefore, if the software industry lacks diversity, software products might unintentionally constrain groups of individuals instead of promoting an equalitarian experience to all. In this study, we investigate one aspect of diversity in the software industry: the perspectives of transgender software professionals about a career in software engineering. Our findings demonstrate that trans people choose careers in software engineering because software development environments are safer and more flexible. However, these professionals face several limitations related to teamwork, such as difficulty in engaging with co-workers and a reduced sense of belonging. Remote and hybrid work in software companies is helping these professionals to cope with these limitations.

Black Box Testing of Futsal Field Rental Information Systems Using Automated Testing Method

A pivotal aspect of software development is testing, which serves as the final phase preceding the release of a software or information system. The realm of software testing encompasses a diverse array of methods, and within the scope of this investigation, the focus is on black box testing. In pursuit of this objective, the study leverages the capabilities of the Katalon Studio for Automated Testing. Its application is directed towards evaluating the functionality of an information system dedicated to the rental of futsal fields in the city of Singkawang. The essence of this examination lies in affirming the integrity of each feature and menu that constitutes the futsal field rental information system. This validation process is integral to ensuring that the system aligns seamlessly with the requisites of its users. The culmination of these testing endeavors culminates in the confirmation that the information system harmoniously resonates with user expectations. Consequently, it stands primed for implementation and subsequent release, signifying the attainment of a pivotal milestone in its development journey.

Optimizing test case prioritization using machine learning algorithms

<p>Software testing is an important aspect of software development to ensure the quality and reliability of the software. With the increasing complexity of software systems, the number of test cases has also increased significantly, making it challenging to execute all the test cases in a limited amount of time. Test case prioritization techniques have been proposed to tackle this problem by identifying and executing the most important test cases first. In this research paper, we propose the use of machine learning algorithms for prioritization of test cases. We explore different machine learning algorithms, including decision trees, random forests, and neural networks, and compare their performance with traditional prioritization techniques such as code coverage-based and risk-based prioritization. We evaluate the effectiveness of these algorithms on various datasets and metrics such as the number of test cases executed, the fault detection rate, and the execution time. Our experimental results demonstrate that machine learning algorithms can effectively prioritize test cases and outperform traditional techniques in terms of reducing the number of test cases executed while maintaining high fault detection rates. Furthermore, we discuss the potential limitations and future research directions of using machine learning algorithms for test case prioritization. Our research findings contribute to the development of more efficient and effective software testing techniques that can improve the quality and reliability of software systems.</p>