Software Development Life Cycle Research Articles

A Fuzzing Tool Based on Automated Grammar Detection

Software testing is an important step in the software development life cycle to ensure the quality and security of software. Fuzzing is a security testing technique that finds vulnerabilities automatically without accessing the source code. We built a fuzzer, called JIMA-Fuzzing, which is an effective fuzzing tool that utilizes grammar detected from sample input. Based on the detected grammar, JIMA-Fuzzing selects a portion of the valid user input and fuzzes that portion. For example, the tool may greatly increase the size of the input, truncate the input, replace numeric values with new values, replace words with numbers, etc. This paper discusses how JIMA-Fuzzing works and shows the evaluation results after testing against the DARPA Cyber Grand Challenge (CGC) dataset. JIMA-Fuzzing is capable of extracting grammar from sample input files, meaning that it does not require access to the source code to generate effective fuzzing files. This feature allows it to work with proprietary or non-open-source programs and significantly reduces the effort needed from human testers. In addition, compared to fuzzing tools guided with symbolic execution or taint analysis, JIMA-Fuzzing takes much less computing power and time to analyze sample input and generate fuzzing files. However, the limitation is that JIMA-Fuzzing relies on good sample inputs and works primarily on programs that require user interaction/input.

КОМБІНОВАНА МОДЕЛЬ ПРОЦЕСУ РОЗРОБЛЕННЯ АПАРАТНО-ПРОГРАМНОГО МОДУЛЯ ОБРОБКИ ДАНИХ СТУДЕНТСЬКОГО НАНОСУПУТНИКА CUBESAT

The CubeSat nanosatellite concept has been a game-changer in the field of space science research and the development of cutting-edge space technologies. The primary factors for their success are low cost, relative simplicity of production, and predictable lifecycle. CubeSats are highly important for preparing future engineers: bachelor's and master's students of aerospace universities. Therefore, the use of CubeSats is a cost-effective way to explore near space and conduct scientific work. However, there are many issues related to the effective development of software, ensuring software quality at the system level, maintenance, and reuse of software code. The advantages and disadvantages of both classical and modern agile (Agile) software development lifecycle models are considered. To enhance flexibility and reduce the complexity of CubeSat projects, a combined model for the development of a hardware-software data processing module is proposed, which combines the advantages of two models: the waterfall model for hardware development and the agile model for software development. For a comprehensive assessment of the combined model of the data processing hardware-software module development process, the SWOT analysis methodology is used, which is a popular strategic planning tool. For its implementation, the strengths and weaknesses, external opportunities, and threats of the combined model of the data processing hardware-software module development process were formulated. The proposed combined model will allow student teams with limited experience to develop hardware-software data processing modules with minimal risks, ensuring code reuse and increasing the attractiveness of student CubeSat projects.

Early Detection of Hard-Coded Secrets in Software Development: A Multi-Method Approach Integrating Static Analysis, Entropy-Based Detection, and Machine Learning

The inadvertent inclusion of hard-coded secrets—such as API keys, passwords, and tokens—within source code poses significant security risks, potentially leading to unauthorized access and data breaches. Recent studies have highlighted the prevalence of this issue; for instance, GitGuardian's 2023 report revealed a 67% increase in detected hard-coded secrets compared to the previous year, with 10 million new secrets discovered in public GitHub commits in 2022. To address this escalating concern, our research proposes a comprehensive, multi-method approach that integrates static code analysis, entropy-based detection, and machine learning techniques to enhance the early identification of hard-coded secrets during the software development lifecycle. By combining these methodologies, the proposed approach improves detection accuracy, reduces false positives, and provides developers with actionable insights to mitigate security vulnerabilities proactively. This integrated strategy not only strengthens the security posture of software applications but also fosters a culture of secure coding practices among development teams. Keywords Shift-Left Security, Secrets Detection, Static Analysis, Entropy-Based Detection, Machine Learning, Hard-Coded Secrets, Secure Development Lifecycle

Empowering Diversity by Building Inclusive Software Engineering Projects with Large Language Models

The integration of emerging AI technologies, particularly Large Language Models (LLMs), is fundamentally reshaping the landscape of software engineering. LLMs offer a wide array of capabilities that can enhance various aspects of software development, including design assistance, automated code analysis and synthesis, and testing. Consequently, their integration into software engineering practices holds the potential to greatly improve efficiency and code quality, marking a notable paradigm shift towards the creation of more intelligent and adaptive systems. This allows the definition of an extended perspective when establishing requirements for building software engineering solutions by incorporating evidence from literature resources and practice. By synthesizing requirements and evidence from scholarly and practitioner sources using LLMs, software engineers can ensure that their solutions are not only technically sound but also align with best practices in fostering social norms and values like inclusivity. Adopting such an approach supports the creation of responsible and inclusive educational software that caters to diverse learning needs and promotes equitable access to educational resources. Moreover, by using LLMs to inform decision-making throughout the software development lifecycle, software engineers can iteratively refine and enhance their solutions based on emerging research findings, thereby ensuring continuous improvement in fostering inclusive educational environments. Hence, this research aims to develop a novel evidence-based software engineering method informed by insights from scientific literature. As a use case, we design and implement a dyslexia-oriented educational software application that supports children in learning to read, guided by this new methodological approach.

Integration of User Experience and Agile Software Development: A Systematic Literature Review

New methods and best practices are being used to enhance the software lifecycle because of the recent expansion of the software industry. Agile development approaches have also been widely used, and they have positively affected people's lives and the operations of enterprises. Although agility makes working with developers easier, it does not offer enough chances to collaborate with users. In early Agile formulations, User Experience (UX) was not recognized as a distinct part of software development. The tricky part is figuring out where to integrate them and how to merge them so that the two parts interact. Combining user experience with Agile approaches is the subject of this research's proposed systematic literature review (SLR). This research found ten relevant studies highlighting the growing interest and research in merging these two critical areas of software engineering. In conclusion, this research found some papers or studies integrating user experience into Agile methodologies. Many techniques are used, and all studies have different approaches to integrate them.

A Hybrid Metaheuristic Aware Enhanced Deep Learning Approach for Software Effort Estimation

Software Effort Estimating (SEE) is a fundamental task in all software development lifecycles and procedures. Therefore, when deciding how to anticipate effort in a variety of project types, the comparative assessment of effort prediction methods has emerged as a standard strategy. Unfortunately, these studies include a range of sample techniques and error metrics, making a comparison with other work challenging. To overcome these drawbacks, this study proposes a deep learning model to effectively estimate software effort. The estimation is mainly focused on minimizing the cost and time consumption. The input data is taken from the dataset and preprocessing is performed to remove the noise content. Then the required features are extracted using the preprocessed data with the help of the simple and higher-order statistical features. A novel Modified Chaotic Enriched Jaya with Moth Flame Optimization (MCEJMO) algorithm is introduced for feature selection to enhance SEE accuracy. The estimation is performed using Multilayer Long Short-Term Memory (M-LSTM). The proposed method achieved a Mean Square Error (MSE) of 0.2825 for dataset 1 and 0.2285 for dataset 2.

Generative AI in the Software Development Lifecycle

Generative AI in the Software Development Lifecycle

Generate High-Coverage Unit Test Data Using the LLM Tool

Unit testing is a critical phase in the software development lifecycle, essential for ensuring the quality and reliability of code. However, the manual creation of unit test scripts and the preparation of corresponding test data can be a time consuming and labor intensive process. To address these challenges, several automated approaches have been explored, including search based, constraint based, random-based, and symbolic execution based techniques for generating unit tests. In recent years, the rapid advancement of large language models (LLMs) has opened new avenues for automating various tasks, including the automatic generation of unit test scripts and test data. Despite their potential, using LLMs in a straightforward manner to generate unit tests may lead to low test coverage. This means that a significant portion of the source code, including specific statements or branches, may remain untested, which can reduce the effectiveness of the tests. To overcome this limitation, the paper presents a novel approach that not only automates the generation of unit test scripts and test data but also improves test coverage. The proposed solution begins by using an LLM tool (such as ChatGPT) to generate initial unit test scripts and data from the source code. To enhance test coverage, the specification document of the source code is also input into the LLM to generate additional test data. Following this, a coverage checking tool is used to evaluate the test coverage and identify untested statements or branches. The LLM is then applied again to generate new test data aimed specifically at addressing these gaps. The initial experimental results indicate that this method significantly improves test coverage, demonstrating its potential to enhance automated unit testing processes.

“My Back Exercise App”—mHealth for Low Back Pain: Development and Usability Testing

Abstract Effective management of chronic low back pain (LBP) often relies on maintaining a healthy lifestyle. Mobile health (mHealth) applications provide a unique opportunity to support LBP sufferers by integrating evidence-based health education into daily routines. However, many existing mHealth apps lack scientific rigour in their development, limiting their impact. This study reports on developing and usability testing My Back Exercise, an mHealth app designed to support individuals managing chronic LBP through a user-centred, theory-based approach. The development followed the Double Diamond framework and Software Development Life Cycle (SDLC) model, focusing on behaviour change theory to promote healthy lifestyle habits. Usability testing involved ten users and employed the System Usability Scale (SUS) and Mobile App Rating Scale (MARS) to assess engagement, functionality, aesthetics, and information quality. Additionally, a think-aloud approach captured real-time feedback and guided iterative improvements. Results showed that the app was well-received, achieving a SUS mean score of 79 (total score of 100) and a MARS mean score of 3.7 (out of 5.0). Key intervention components included exercises, sleep management, diet, and pain education, contributing to a holistic self-management tool. This study provides a transparent roadmap for the development of evidence-based digital health tools. The My Back Exercise app offers an innovative, holistic approach to help people self-manage chronic LBP using evidence-based methods.

The Role of Artificial Intelligence in Modern Software Engineering

Abstract. The rapid advancement of Artificial Intelligence (AI) has significantly influenced various industries, including software engineering. This paper explores the integration of AI into software engineering, focusing on its applications across different stages of the software development life cycle, including design, development, testing, project management, and maintenance. AI's ability to automate tasks, enhance efficiency, and improve code quality is revolutionizing how software is built and maintained. The paper also addresses the challenges and risks associated with AI-driven software engineering, such as dependency on AI tools, ethical concerns, and security vulnerabilities. Finally, the paper highlights future trends in AI-powered software engineering, including adaptive and self-healing systems, AI-enhanced collaboration, and full software automation. The role of AI in shaping the future of software engineering is both profound and transformative, making it a critical area of study.

Aplikasi Website dengan Flask dan Open CV untuk Filtering Warna Bagi Penderita Buta Warna

Color blindness is a hereditary vision disorder that impairs the ability to distinguish certain colors, significantly affecting daily activities and quality of life. This study aims to develop a web-based application using Flask and OpenCV to assist individuals with color blindness in identifying colors accurately. The application incorporates image processing technology to enhance color contrast and simulate real-time color perception adjustments. Employing the Waterfall model of the Software Development Life Cycle (SDLC), the study encompasses requirements analysis, system design, implementation, testing, and maintenance. Key features include Camify, for real-time color adjustments via device cameras, and Pickerify, for detecting colors in uploaded or live images. Testing reveals the application's effectiveness in providing improved color perception for users with various types of color blindness (e.g., Deuteranopia, Protanopia, Tritanopia). Despite minor limitations under extreme lighting conditions, the intuitive user interface and robust functionality make the application accessible to diverse user groups. Future enhancements include integrating AI for personalized filters and expanding compatibility with emerging technologies.

A Review of Non-Functional Requirements Analysis Throughout the SDLC

To date, unquestionable efforts have been made, both in academia and industry, to facilitate the development of functional requirements (FRs) throughout the different phases of the software development life cycle (SDLC). Functional requirements are understood to mean the users’ needs pertaining to the services to be rendered by a software system. For example, semi-formal or graphically based approaches such as UML, and mathematically based or formal approaches such as Z and related tools have all been developed with the intention of addressing FRs. In the same vein, most of the proposed software methodologies, for instance, agile software development and model-driven software development, primarily target functional requirements. Considering the importance and even the criticality of non-functional requirements describing the quality of software systems and the constraints upon them, similar progress would be expected for their development. However, it appears that making headway with NFRs has been more challenging due to the complexity of the requirements. In this regard, the main purpose of this work is to unveil (from the academic perspective) the current state of development of NFRs through the review of publications carefully selected from five online databases.

AI‐Augmented Software Engineering: Revolutionizing or Challenging Software Quality and Testing?

ABSTRACTWith organizations seeking faster, cheaper, and smarter ways of delivering higher quality software, many are looking towards generative artificial intelligence (AI) to drive efficiencies and innovation throughout the software development lifecycle. However, generative AI can suffer from several fundamental issues, including a lack of traceability in concept generation and decision‐making, the potential for making incorrect inferences (hallucinations), shortcomings in response quality, and bias. Quality engineering (QE) has long been utilized to enable more efficient and effective delivery of higher quality software. A core aspect of QE is adopting quality models to support various lifecycle practices, including requirements definition, quality risk assessments, and testing. In this position paper, we introduce the application of QE to AI systems, consider shortcomings in existing AI quality models from the International Organization for Standardization (ISO), and propose extensions to ISO models based on the results of a survey. We also reflect on skills that IT graduates may need in the future, to support delivery of better‐quality AI.

Advanced Security Strategies for Software Vulnerability Protection: A Multi-Scale Detection Model With FPGA-RFID

This paper proposes a multi-scale software vulnerability detection model using Radio Frequency Identification (RFID) target recognition technology and Field-Programmable Gate Array (FPGA) hardware design. The systematic analysis of RFID systems and FPGA architectures provides the foundation for constructing robust security solutions against code-based threats. The Scalable Vulnerability Detection Method (SVDM) is developed leveraging multi-scale code metrics and deep feature extraction techniques. The evaluations of vulnerability datasets CWE-119 and CWE-399 demonstrated over 84% accuracy, recall, and F1 score. The precision, recall, and F1 score of the CWE-399 vulnerability dataset in the SVDM were 85.15%, 85.12%, and 84.79%, respectively, while the values of the CWE-199 dataset were 83.12%, 82.16%, and 82.38%, respectively. Compared with existing methods, the functionality of SVDM was further highlighted. This research provides an extensible and adaptable framework for identifying security vulnerabilities with both hardware and software techniques. It can optimize threat detection, risk analysis, and system integrity across software development and operation life cycles, providing technical reference and scientific support for the security protection of software vulnerabilities, while promoting the development of industries such as intelligent monitoring and traceability.

Transforming Software Development Through Generative AI : A Systematic Analysis of Automated Development Practices

This article presents a comprehensive analysis of the transformative impact of Generative Artificial Intelligence (GenAI) on software development practices. Through systematic evaluation of implementations across multiple development environments, this article demonstrates how GenAI technologies significantly enhance four critical areas of the software development lifecycle: code generation, automated testing, intelligent code review, and predictive failure analysis. The findings indicate a 45% reduction in initial code development time through automated code generation, 60% improvement in test coverage through AI-driven test case creation, and 30% decrease in post-deployment issues through predictive failure analysis. The article employs a mixed-methods approach, combining quantitative analysis of development metrics from 50 software projects with qualitative assessments from 200 professional developers across diverse organizational contexts. Results reveal that organizations implementing GenAI-assisted development workflows experience a mean productivity increase of 37% while maintaining or improving code quality metrics. Additionally, the article identifies key integration challenges and provides a framework for effective GenAI adoption in software development environments. These findings contribute to the growing body of knowledge on AI-assisted software development and offer practical insights for organizations seeking to optimize their development processes through GenAI integration.

Integrating the Software Development Lifecycle into Work-Integrated Learning:

Work-integrated learning is crucial for student development, providing hands-on experience in industry placements or project-based learning at higher educational institutions. This article evaluates the impact of adopting a Software Development Lifecycle (SDLC) process in work-integrated learning software development projects at a University of Technology. Comparing two student projects, the waterfall model resulted in substandard conversion between class diagrams and route classes, while the agile model allowed frequent reviews and modifications, resulting in alignment between class diagrams and route classes. The key difference was the absence of clear directional indicators and comprehensive descriptions of class relationships. It is recommended that students adopt an appropriate SDLC model that meets their specific project needs within a work-integrated learning environment.

PERANCANGAN SISTEM INFORMASI ANALIS BREAK EVEN POINT DAN STOK PADA BUDIDAYA AYAM POTONG BERBASIS WEB

Although broiler chicken farming offers many benefits, there are also risks of losses, especially when production costs are not accurately recorded, leading to inaccurate per-unit pricing that can harm farmers. To assist farmers in accurately determining the Break Even Point (BEP) price for each chicken, a web-based cost and stock management information system for broiler farming has been developed. To provide a clear and sequential software lifecycle approach, this system was built using the waterfall method, which includes the stages of requirements analysis, design specifications, coding, testing, and maintenance. Each stage produces measurable outcomes before proceeding to the next. The development results indicate that the system successfully records the total production costs, including detailed costs for feed, medicine, operational expenses, and chicken stock. By using the formula BEP = total production cost/number of live chickens, the system achieves accurate pricing for broiler chickens. Based on these findings, future research is suggested to improve the user interface for greater appeal, optimize system accessibility on mobile devices, and integrate this system with other systems.

Forecasting of Menstruation using a Calendar-based Method based on a Web Application

Accurately predicting menstrual dates is valuable for women, yet it poses a significant challenge, particularly for those with irregular cycles. This study introduces a web application that utilises a calendar-based method to predict upcoming menstrual dates while offering additional features such as tracking past periods, logging symptoms, and providing personalised health advice. Using the software development life cycle approach, the critical contribution of this work is the development of a user-friendly tool that not only aids in menstrual tracking but also highlights the limitations of calendar-based predictions, especially for women with irregular cycles. Our findings suggest that while the method provides accurate predictions for women with regular cycles, it is less reliable for those with irregular cycles due to its dependence on a fixed cycle length range. This study underscores the need for more adaptive models to improve prediction accuracy for a broader population.

Evaluating the Role of Data Privacy Regulations in Secure Software Development Life Cycles (SDLC)

Evaluating the Role of Data Privacy Regulations in Secure Software Development Life Cycles (SDLC)

DevSecOps practices and tools

Nowadays, software development happens at a fast pace. At the same time, Information Technology organizations face higher demands and competition while struggling with external threats such as cyberattacks. Therefore, many organizations adopt DevOps as a working culture to improve their Software Development Lifecycle (SDL). However, the success of DevOps adoption remains inconsistent, and recently, IEEE introduced a DevOps standard that might help improve DevOps adoption. The standard mentions DevSecOps as the security aspect of DevOps, adding security practices to the SDL from inception, but what are these practices or capabilities? Which tools can be used to implement these practices? Therefore, a Multivocal Literature Review was performed to identify DevSecOps practices and their definitions, and which tools can be used to implement them.