Software Development Process Research Articles

The Future of AI-Driven Software Engineering

A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.

Automated System for Evaluating Alternatives for Developing Innovative IT Projects

Software engineering occupies a prominent place in the theory and practice of simulation modeling, which necessitates scientific research in the field of methodological principles for forming software product quality. The problem of determining the optimal option for software development is one of the key ones in the field of information technology because it determines the quality of the final product and the efficiency of project management. The article considers the concept of developing an automated system, the basis of which is the software for assessing alternatives in the process of creating innovative IT projects. The main goal of the study is to model alternatives and select the optimal option for the process of creating an IT project using modern methodological approaches. For this purpose, the methods of ontological analysis, expert evaluation, multi-criteria optimization, pairwise comparisons and multi-factor selection of alternatives are applied. In the course of the research, a subset of Pareto factors is singled out and alternative development options are formed based on the method of linear convolution of criteria. The proposed methodology allows for assessing the importance of key factors and selecting the optimal option for the software development process. As a result, the developed approach contributes to strategic planning and increases the transparency of the decision-making process. The key result of the research is the created software product that allows one to automate the procedure for selecting the optimal solution for the IT project development process, providing reliable support for simulation modeling and increasing the efficiency of project management. The proposed methodology creates a new paradigm for making informed decisions regarding systems for creating complex software complexes.

Systematic Literature Review on Methodologies for Improving Software Quality in Software Development Process

For better repute in software monopoly, qualityof product is one of the essential factors in any corporation.Quality of the product depends on the satisfaction of customer.Product quality is measured by customer satisfaction with thatproduct. The goal of quality might be attained only overconvenient fundamentals and policies. The enormous anddeveloping nations are making improvements on every day forimproving the quality of the products they manufactured toincrease the satisfaction of their customers. Every corporationtries their best to design quality software that best suits tocustomer needs. For the purpose of better quality, somefundamentals and policies were made, however there might bedistinct problems, there are several rationales for poor qualityof product. We choose quantitative approach of research asmethodology for our research. Basically, quantitative approachfocuses on measuring and evaluating the data. This paper willaddress different issues that were corresponding to theserationales. Several techniques will also be explained, as wellsome alternative solutions are defined in this paper that willleads to good quality of any product or software. Furthermore,we conclude that systematic approach and incrementalapproach, these are good but the problem with these approachesis that requirements will be freeze. It will be complex andproblematic to accommodate changes. In short, all techniqueshave their own impact on enhancement of software quality.

Optimizing Public Services through Industrial Work Practice Management Applications Using Extreme Programming Methods

Industrial work practice or industrial Internship is applying knowledge and skills to solve real problems. It becomes one of the efforts to improve the quality of human resources in an agency or company. Managing the industrial Internship at PT Artha Abadi is still being carried out using physical documents. This allows errors to occur in recording Industrial Internship data and documents. This study aimed to overcome existing problems so that the management of Industrial Internship activities at PT Artha Abadi is more systematic, organized and efficient. The method used in creating this system is the Extreme Programming method which aims to increase effectiveness and efficiency in the software development process. The Industrial Internship Management Application is built using the Laravel framework and MySQL as its database. The tools used to create this system are XAMPP as a database server and Visual Studio Code for a text editor, in system testing using Black Box Testing to test the software's functionality. Testing shows that the Industrial Internship Management Application does not experience system errors or bugs. The results of this study show that the application is built according to the main objectives and is ready to be implemented so that the Industrial Internship at PT Artha Abadi runs more systematically, organized, and efficiently.

Emphasizing the Early Phases of the Software Development Process Before Deploying Smart Contracts

Emphasizing the Early Phases of the Software Development Process Before Deploying Smart Contracts

A Mobile Secure Content Development Application for Children in the Software Development Life Cycle Framework

This study deals with the software development process of a mobile application that will support children's learning, development and entertainment processes while ensuring their safety in the online environment. The project was carried out within the framework of the Software Development Life Cycle (SDLC) and adopted a systematic approach including requirements analysis, design, development, testing and maintenance phases. The application aims to provide appropriate content for children by minimizing digital security risks and to enable parents to safely control their children's internet use. With the Extreme Programming (XP) methodology, a continuous improvement process based on user feedback was implemented and security and performance tests were successfully completed. The article shows that not only coding but also effective management of the whole process in software development projects contributes to project success. The study is expected to contribute to the literature on children's digital safety and provide a basis for future research in this field.

Integration of the double diamond methodology of design thinking and devops for the optimization of software development processes

Integration of the double diamond methodology of design thinking and devops for the optimization of software development processes

Java coding using artificial intelligence

This study explores the potential of chatbots, specifically ChatGPT, in Java software development. The aim is to classify tasks for effective use of industrial code and develop recommendations for applying chatbot assistance, identifying boundaries where human intervention remains essential. The methodology included analyzing scientific literature and empirically testing ChatGPT-3.5 on various Java development tasks. The tasks were divided into simple (working with XML, JSON, multithreading, and data input/output) and complex (writing MVC applications, REST services, and GUI). The results showed that ChatGPT successfully handles simple tasks but struggles with complex problems. The study identified scenarios where the chatbot can effectively use existing codebases and design patterns to accelerate development. The conclusions highlight ChatGPT’s potential in improving developer productivity, optimizing certain development tasks, and more efficiently allocating human resources in projects. However, the study also points out the need for human intervention to verify, correct, and improve generated code. The study contributes to understanding the practical usefulness of chatbots in real development scenarios and offers recommendations for integrating AI tools into the software development process.

Technical Credit

Balancing initial investment and long-term results in the software development process.

Ecore4PROV-DM: A Metamodel for Enhancing Data Provenance Adoption in Information Systems

Effective management of data provenance is essential in Information Systems, particularly for data-intensive applications. Despite the W3C PROV family of documents establishing a standard for representing provenance, integrating this information into software development processes remains a significant challenge. This paper addresses the problem by introducing the Ecore4PROV-DM metamodel, developed using Model-Driven Engineering techniques to align with the W3C PROV data model (PROV-DM). The metamodel's application is demonstrated through real-world scenarios, including the Urban Observatory project at Newcastle University. Evaluated using a subset of the Metamodel Quality Requirements and Evaluation (MQuaRE) framework, focusing on three key quality requirements, Ecore4PROV-DM exhibits high accuracy and completeness, making it a robust tool for provenance modeling. By bridging the gap between the conceptual richness of W3C PROV-DM and practical implementation needs, Ecore4PROV-DM facilitates precise provenance representation and seamless integration into diverse Information Systems.

DEFECT SEVERITY CODE PREDICTION BASED ON ENSEMBLE LEARNING

In machine learning, learning algorithms that learn from other algorithms are called meta-learning. New algorithms called Ensemble algorithms have surfaced as a viable method to improve defect prediction models' accuracy and dependability. In software development defect prediction of software engineering is still a big challenge, and leads to the failure of systems, increases the cost of maintenance, and makes the development process more difficult. Consequently, defect prediction systems have become more popular as a way to foresee possible flaws early on in the development process. Defect prediction is the process that specifies the possible defects in the code written newly or the existing modified code without the use of code testing. This paper introduces ensemble learning ideas, reviews the traditional defect prediction models, and investigates ensemble learning techniques for defect classification and prediction such as bagging, boosting, stacking, and random forests, Case studies and actual experiments illustrate the important role of ensemble algorithms in classifying five severity types of defects and predicting the severity code of defects to improve the software development process by reducing the time and effort needed to determine the type of defect.

Automatic Bug Triaging Process: An Enhanced Machine Learning Approach through Large Language Models

Bug resolution and maintenance are the most critical phases of the software development life cycle. The traditional bug triaging concept refers to the manual assignment of bugs to the appropriate developer after reading the bug details from the bug tracker and further resolving it. The advent of machine learning algorithms provides various solutions for automated bug triaging. Machine learning algorithms can be used to classify bugs and assign each to a developer. Reducing manual efforts optimizes bug-triaging by utilizing manpower in other software development processes. Furthermore, machine learning Large Language Models (LLMs) can be used to take advantage of their natural language processing features and capabilities. This study proposes a machine learning-based embed chain LLM approach for automatic bug triaging. This approach is used to automatically classify bug reports. Based on the results, the appropriate developer is recommended. In addition, the proposed approach is used to automatically predict the priority of bug reports. This paper also discusses the strengths and challenges of the proposed approach.

A New Approach to the Quality Determination of Used Palm Cooking Oil using Supervised Learning based on Electronic Sensors

As discarding used palm oil in nature is very dangerous, a processing mechanism is needed to utilize it according to needs. This utilization depends on the palm oil used, so the sorting process becomes important. This study proposes a new classification approach for the quality of used palm oil using Self-Organizing Map (SOM), Linear Vector Quantization (LVQ), and K-means, based on electronic sensors. This study included hardware design, software development, data collection, and training and testing processes. Based on the experimental results, the proposed system performed well using 13 parameters consisting of e-nose data, color, viscosity, and turbidity. The accuracy of SOM was 91.11%, LVQ achieved 95.56%, and K-Means obtained an accuracy of 98.89%. This system can be used as a decision support system in the automatic recognition of used palm oil to classify its quality.

MULTICR: Predicting Merged and Abandoned Code Changes in Modern Code Review Using Multi-Objective Search

Modern Code Review (MCR) is an essential process in software development to ensure high-quality code. However, developers often spend considerable time reviewing code changes before being merged into the main code base. Previous studies attempted to predict whether a code change was going to be merged or abandoned soon after it was submitted to improve the code review process. However, these approaches require complex cost-sensitive learning, which makes their adoption challenging since it is difficult for developers to understand the main factors behind the models’ predictions. To address this issue, we introduce in this article, MULTICR , a multi-objective search-based approach that uses Multi-Objective Genetic Programming (MOGP) to learn early code review prediction models as IF-THEN rules. MULTICR evolves predictive models while maximizing the accuracy of both merged and abandoned classes, eliminating the need for misclassification cost estimation. To evaluate MULTICR, we conducted an empirical study on 146,612 code reviews from Eclipse, LibreOffice, and Gerrithub. The obtained results show that MULTICR outperforms existing baselines in terms of Matthew Correlation Coefficient (MCC) and F1 scores while learning less complex models compared to decision trees. Our experiments also showed how MULTICR allows identifying the main factors related to abandoned code reviews as well as their associated thresholds, making it a promising approach for early code review prediction with notable performance and inter-operability. Additionally, we qualitatively evaluate MULTICR by conducting a user study through semi-structured interviews involving 10 practitioners from different organizations. The obtained results indicate that 90% of the participants find that MULTICR is useful and can help them to improve the code review process. Additionally, the learned IF-THEN rules of MULTICR are transparent.

Automation in Cloud-Based DevOps: A Guide to CI/CD Pipelines and Infrastructure as Code (IaC) with Terraform and Jenkins

This study offers a comprehensive analysis of automation in cloud-based DevOps, focusing on the role of Continuous Integration/Continuous Delivery (CI/CD) pipelines and Infrastructure as Code (IaC) in streamlining software development processes. Employing tools like Jenkins and Terraform, the research aims to demonstrate how automation can significantly enhance operational efficiency, scalability, and security in cloud deployments. Through a detailed examination of CI/CD components and their integration with IaC, this paper identifies key findings, including the reduction of manual errors, improved deployment consistency across environments, and enhanced security through DevSecOps practices. The study further explores challenges such as configuration complexity and compliance, proposing best practices like proactive monitoring, encrypted secrets management, and version control to mitigate these issues. Conclusively, the research recommends the adoption of AI-driven analytics and robust security frameworks to optimize cloud-based CI/CD automation. This work not only highlights current methodologies but also anticipates future trends, providing a strategic roadmap for organizations aiming to leverage DevOps automation effectively.

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.

Towards a Generic Software Architecture for IoT Systems

The complexity of IoT, owing to the inherent distributed and dynamic nature of such systems, brings more challenges to the software development process. A vast number of devices with different communication protocols and data formats is involved and needs to be connected and exchange data with each other in a seamless manner. Traditional software architectures fall short of addressing the requirements of IoT systems and, therefore, a new approach to software architecture is required. This paper presents an attempt to lay out the foundation for a quality attribute driven software architecture for the development of IoT systems. This architecture accommodates the appropriate architectural styles and design patterns necessary for the development of a robust IoT system. These include edge computing, microservices and event driven architectures. The proposed architecture treats IoT systems as autonomic systems which require a closed control loop to regulate and orchestrate the operational aspect of the IoT system.

Predicting Tags for Learner Questions on Stack Overflow

AbstractOnline question answering sites, such as Stack Overflow (SO), have become an important learning and support platform for computer-science learners and practitioners who are seeking help. Learners on SO are currently faced with the problem of unanswered questions, inhibiting their lifelong-learning efforts and contributing to delays in their software development process. The major reason for this problem is that most of the technical problems posted on SO are not seen by those who have the required expertise and knowledge to answer a specific question. This issue is often attributed to the use of inappropriate tags when posting questions. We developed a new method, BERT-CBA, to predict tags for answering user questions. BERT-CBA combines a convolutional network, BILSTM, and attention layers with BERT. In BERT-CBA, the convolutional layer extracts the local semantic features of an SO post, the BILSTM layer fuses the local semantic features and the word embeddings (contextual features) of an SO post, and the attention layer selects the important words from a post to identify the most appropriate tag labels. BERT-CBA outperformed four existing tag recommendation approaches by 2-73% as measured by F1@K=1-5. These findings suggest that BERT-CBA could be used to recommend appropriate tags to learners before they post their question which would increase their chances of getting answers.

Mining Fine-Grained Code Change Patterns Using Multiple Feature Analysis

Maintaining high code quality is a crucial concern in software development. Existing studies demonstrated that developers frequently face recurrent bugs and adopt similar fix measures, known as code change patterns. As an essential static analysis technique, code pattern mining supports various tasks, including code refactoring, automated program repair, and defect prediction, thus significantly improving software development processes. A prevalent approach to identifying code patterns involves translating code changes to edit actions into a Bag-of-Words (BoW) model. However, when applied to open-source projects, this method exhibits several limitations. For instance, it overlooks function call information and disregards feature word order. This study introduces MIFA, a novel technique for mining code change patterns using multiple feature analysis. MIFA extends existing BoW methods by incorporating analysis of function calls and overall changes in the Abstract Syntax Tree (AST) structure. We selected 20 popular Python projects and evaluated MIFA in both intra-project and cross-project scenarios. The experimental results indicate that: (1) MIFA achieved higher silhouette coefficients and F1 scores compared to other state-of-the-art methods, demonstrating a superior accuracy; (2) MIFA can assist developers in detecting unique change patterns more earlier, with an efficiency improvement of over 40% compared to random sampling. Additionally, we discussed critical parameters for measuring the similarity of code changes, guiding users to apply our method effectively.

Contributing to Organizational Performance Through Digital Human Resource Management

Organizations have embraced digitalization as a way of optimizing their processes, specially the one related to human resource management since it helps ensuring organizations remain effective and competitive. In the Cuban context, digitalization is conceived within the <i>National Development Plan until 2030</i> and the <i>Comprehensive Policy for the Improvement of the Informatization of Society in Cuba </i>facilitating its rapid dissemination and adoption in different sectors<i>.</i> However, little attention has been paid to subject of digital Human Resource Management (HRM) in Cuban institutions. This article aims at providing scientific evidence concerning the implementation of digital HRM in a Cuban institution, specifically at Universidad Central “Marta Abreu” de Las Villas. In order to conduct the study, it was necessary to divide it into two parts: one related to the identification of the theoretical and methodological foundations concerning the digitalization of HRM worldwide and within the Cuban context and the second part was devoted to the software development process. In both cases methods such as literature review, historical-logical, analysis and synthesis, participant observation and interviews were applied. As a result, the software P.R.O.G.R.E.S.S was developed, successfully validated by the Human Resources Directorate personnel who highlighted its contributions to the university´s organizational performance since it helps fostering a culture of mentorship by pairing less experienced instructors with seasoned faculty members; creating initiative programs for faculties that excel in teaching, research or community engagement; and facilitating benchmarking against institutions by providing comprehensive reports on faculty achievements. Some suggestions are given for further research.