Software Development Research Articles

Optimizing API Project Efficiency: Agile Configurations, Advanced Design Patterns, and Testing Strategies

In the rapidly evolving digital era, Application Programming Interfaces (APIs) have emerged as the cornerstone of modern software development, enabling seamless communication and data exchange between disparate systems. Optimizing API project efficiency is paramount for organizations aiming to stay competitive and agile in response to market demands. This paper explores strategies for enhancing API efficiency through agile configurations, advanced design patterns, and comprehensive testing methodologies. We delve into the significance of decoupling configurations to achieve agile adaptability, discuss various API design paradigms like RESTful APIs and GraphQL, and examine their specific use cases and benefits. Additionally, we emphasize the importance of robust testing and security practices, highlighting industry-standard tools and technologies that facilitate these processes. Real-world case studies are presented to illustrate practical implementations, and the tangible improvements achieved. By integrating these advanced strategies, organizations can enhance their API performance, scalability, and maintainability, thereby delivering superior value to users and stakeholders in an increasingly interconnected world. Keywords: API efficiency, agile adaptability, decoupling configurations, RESTful API, GraphQL, API testing, microservices, API gateway, performance optimization, configuration management, API security, authentication, automated testing, real-world case studies

A Framework for Real-Time AI-Driven Secure Code Analysis Integrated with DevSecOps in Cloud-Native CI/CD Pipelines

Abstract—This paper presents a novel framework for inte- grating real-time AI-driven secure code analysis into DevSecOps practices within cloud-native CI/CD pipelines. As organizations increasingly adopt cloud- native architectures and agile develop- ment methodologies, the need for robust, automated security mea- sures becomes paramount. Our proposed framework leverages advanced machine learning algorithms to perform continuous, real-time code analysis, identifying potential vulnerabilities and security risks throughout the development lifecycle. By seamlessly integrating with existing CI/CD tools and cloud platforms, our solution enables organizations to enforce security policies, detect threats, and remediate issues without compromising development velocity. We evaluate the effectiveness of our framework through a series of case studies across diverse software projects, demon- strating significant improvements in threat detection accuracy, reduced false positives, and overall security posture. Our results indicate that the proposed AI-driven approach can enhance code security by up to 40% compared to traditional static analysis tools, while maintaining the agility of modern development prac- tices. This research contributes to the evolving field of DevSecOps by offering a scalable, intelligent solution for embedding security into the heart of cloud-native software development processes. Index Terms—DevSecOps, AI-driven security, cloud- native, CI/CD pipelines, secure code analysis

An assessment of large language models for OpenMP-based code parallelization: a user perspective

Large language models have sparked a lot of attention in the research community in recent days, especially with the introduction of practical tools such as ChatGPT and Github Copilot. Their ability to solve complex programming tasks was also shown in several studies and commercial solutions increasing the interest in using them for software development in different fields. High performance computing is one of such fields, where parallel programming techniques have been extensively used to utilize raw computing power available in contemporary multicore and manycore processors. In this paper, we perform an evaluation of the ChatGPT and Github Copilot tools for OpenMP-based code parallelization using a proposed methodology. We used nine different benchmark applications which represent typical parallel programming workloads and compared their OpenMP-based parallel solutions produced manually and using ChatGPT and Github Copilot in terms of obtained speedup, applied optimizations, and quality of the solution. ChatGPT 3.5 and Github Copilot installed with Visual Studio Code 1.88 were used. We concluded that both tools can produce correct parallel code in most cases. However, performance-wise, ChatGPT can match manually produced and optimized parallel code only in simpler cases, as it lacks a deeper understanding of the code and the context. The results are much better with Github Copilot, where much less effort is needed to obtain correct and performant parallel solution.

A Multimodal conceptual framework to achieve automated software evolution for context-rich intelligent applications

AbstractWhile AI is extensively transforming Software Engineering (SE) fields, SE is still in need of a framework to consider overall all phases to facilitate Automated Software Evolution (ASEv), particularly for intelligent applications that are context-rich instead of conquering each division independently. Its complexity comes from the intricacy of the intelligent applications, the heterogeneity of the data sources, and the constant changes in the context. This study proposes a conceptual framework for achieving automated software evolution, emphasizing the importance of multimodality learning. A Selective Sequential Scope Model (3 S) model is developed based on the conceptual framework, and it can be used to categorize existing and future research when it covers different SE phases and multimodal learning tasks. This research is a preliminary step toward the blueprint of a higher-level ASEv. The proposed conceptual framework can act as a practical guideline for practitioners to prepare themselves for diving into this area. Although the study is about intelligent applications, the framework and analysis methods may be adapted for other types of software as AI brings more intelligence into their life cycles.

Decision Support System for the Design Process of Apatite Biopolymer Composite Parts

Abstract In connection with the increase in the number and severity of various types of bone tissue injuries received as a result of wounds during military operations in Ukraine, an important issue in orthopedics and traumatology is making informed decisions about the possibility of restoring the integrity and functions of bone tissue when using different types of composition, porosity and strength of apatite-biopolymer composites. The scientific direction of research is the development of principles and methods for making scientifically based decisions in the design and additive manufacturing of bone substitutes based on apatite-biopolymer composites with functional properties depending on the nature of the localization of the cavity bone defect and its size. A set of methods for analyzing images of bone tissue, taking into account its spatial structure, which are obtained by sensors of different physical nature, with the use of neural network models, development of methods of their design, optimization and training is proposed. The new knowledge obtained as a result of the project will become the necessary basis for making optimal decisions in practice for the introduction of the latest methods of treatment and prosthetics in trauma surgery, oncology, cranio-maxillofacial surgery, dentistry, taking into account the risks of biocompatibility of apatite-biopolymer composites. Software development of an intelligent decision support system will be used to design bone substitutes with controlled composition, structure, porosity and mechanical strength for the further selection of additive technology for its production from apatite-polymer composites, which will contribute to increasing the efficiency of treatment and prosthetics in orthopedics and traumatology.

An open codebase for enhancing transparency in deep learning-based breast cancer diagnosis utilizing CBIS-DDSM data

Accessible mammography datasets and innovative machine learning techniques are at the forefront of computer-aided breast cancer diagnosis. However, the opacity surrounding private datasets and the unclear methodology behind the selection of subset images from publicly available databases for model training and testing, coupled with the arbitrary incompleteness or inaccessibility of code, markedly intensifies the obstacles in replicating and validating the model's efficacy. These challenges, in turn, erect barriers for subsequent researchers striving to learn and advance this field. To address these limitations, we provide a pilot codebase covering the entire process from image preprocessing to model development and evaluation pipeline, utilizing the publicly available Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) mass subset, including both full images and regions of interests (ROIs). We have identified that increasing the input size could improve the detection accuracy of malignant cases within each set of models. Collectively, our efforts hold promise in accelerating global software development for breast cancer diagnosis by leveraging our codebase and structure, while also integrating other advancements in the field.

The Development of Operational Model Integrating Digital Technologies into Public Mental Healthcare Services in Nigeria

The availability and utilization of digital technologies can leverage the challenges of the sharp-depleting workforce due to the consistent migration of healthcare professionals (HCPs), the alarming ratio of the number of patients to HCPs, discretional use of digital technologies among HCPs in providing effective therapeutic interventions and mental healthcare services. This study aimed to develop an operational model that integrates digital technologies in mental healthcare service delivery for public facilities in Nigeria. The practice-oriented theory guides the development of the model. A qualitative method assessed the digital technologies used during the COVID-19 pandemic. A non-probability, purposive sampling technique that employed the snowball method was used in selecting the sixteen (16) HCPs from four (4) neuropsychiatric hospitals in South-West Nigeria. Thematic analysis was conducted on qualitative data. Findings from the qualitative analysis revealed several challenges in providing adequate and efficient services in public mental healthcare facilities in Nigeria, such as human resource challenges, brain drain, lack of infrastructure, and strains on HCPs. Findings from this study indicated that digital technologies are sparingly utilized in public mental healthcare service delivery due to a lack of policy and framework. HCPs perceive digital technologies to be beneficial to themselves and their clients. The highlighted benefits to patients are reduced stigmatization levels, decreased financial burden, enhanced accessibility to therapists, strengthened adherence, mitigated relapse incidences and improved intervention. The transition from the traditional/conventional space of mental healthcare service delivery to digital space requires a collaborative approach with Computer scientists (software developers, database administrators), Data scientists, investors, stakeholders and mental HCPs. The proposed model serves as a contemporary framework that the Government, policymakers in the Ministry of Health and management of mental health facilities in Nigeria can adopt, providing the needed awareness of the benefit of technological intervention.

Enhancing the Robustness of the E_BLIND/D_LC Digital Watermarking Algorithm Against DCT-Filtering Removal Attacks

The paper focuses on evaluating and improving the robustness of watermarking algorithms against DCT-filtering, a common image processing technique that can be used to remove embedded watermarks from cover images. The study examines the EBLIND/DLC watermarking algorithm, which operates in the spatial domain and is designed to embed a 1-bit message into an image. To evaluate robustness against DCT-filtering, the research utilizes software implementations of the watermarking algorithms, the dctdnoiz filter from FFmpeg, and the Flickr8k image database, which contains 8091 images. An extension to the E_BLIND/D_LC algorithm is proposed to enhance its robustness against DCT-filtering. The paper presents an experiment comparing the robustness of the extended algorithm with the original E_BLIND/D_LC. The results demonstrate that the extended algorithm improves robustness against DCT-filtering and also maintains better image quality, as measured by the PSNR metric, at equal levels of robustness in terms of the number of successful watermark detections after filtering. This suggests that the proposed extension effectively balances enhanced robustness against DCT-filtering with the preservation of image quality. The experiment confirms that the extended version outperforms the original algorithm in both detection reliability and image quality under DCT-filtering conditions. The paper includes links to the algorithm implementations on C++ programming language and the Python testing scripts, which can be useful for further research and practical applications in software development.

Development of Software for Hydrometallurgical Calculation of Metal Extraction

Hydrometallurgy plays a critical role in the metallurgical industry by providing an efficient method for extracting metals from ores and secondary materials using aqueous solutions. This approach is particularly advantageous for processing low-grade and complex ores, as well as secondary resources that cannot be effectively processed by traditional pyrometallurgical methods. The objective of this study is to develop specialized software to automate and optimize the calculations necessary for metal extraction in hydrometallurgical processes. The software integrates a complete computational framework for automating the various stages of the hydrometallurgical process, including ore composition initialization, total element mass calculations, and the determination of metal concentrations in both metal products (matte) and by-products (slag). The methodology involves the design and implementation of a web-based application using Django for the user interface and MySQL for robust data storage. The computational module, written in Python, automates the mathematical operations required to simulate complex chemical reactions and metal extraction processes. This module supports real-time processing and ensures accurate calculations for each stage of metal extraction, including leaching, extraction, re-extraction, sorption, and electrolysis. The software also features detailed tables and dynamic graphs that allow users to analyze the distribution of valuable metals and assess the influence of key operational parameters on extraction efficiency. The results show that the developed software successfully manages large datasets, enhances the precision of hydrometallurgical calculations, and minimizes the risk of human error. The implementation of the software leads to significant improvements in the economic efficiency of production by optimizing metal recovery rates and reducing operational costs. Additionally, it supports comprehensive process control by providing actionable insights into each stage of extraction, thus improving the quality of the final metal product. Overall, the software represents a significant technological advancement in the field, offering a scalable solution for industries aiming to streamline hydrometallurgical processes.

Penerapan Model Incremental Delivery pada Pengembangan Aplikasi Sistem Informasi Hotel Cipta Jakarta

Along with the development of the times, we see many hotels experiencing changes. A change that is rarely reviewed as a news topic is the software overhaul that occurs in hotels from offline to online systems. Lay people only see changes in hotels from a visible perspective, without having to know whether the hotel's system and system applications have changed. One of the factors in system development at Hotel Cipta is the need for hotel management to prioritize the development of system applications from offline to online systems, even web-based. It is not easy to overhaul an existing system application compared to creating a new system application. There are several software engineering methods and models that are commonly used to analyze system needs and design systems in the software development process. One of them is the incremental delivery model. This makes it interesting to try to apply the incremental delivery model to the development of the Hotel Cipta information system application by taking place at the Hotel Cipta on KH. Wachid Hasyim Street, Central Jakarta. In this study, the object of research is the Front Office department.

Cracking the code: Linking good modeling and coding practices for new ecological modelers

Good modeling practices are essential for producing reliable and reproducible ecological models. Inherent to good modeling practices are fundamental coding and documentation skills, which not only implement the desired modeling capabilities but also clearly outline the goals, methods, and components of a model that are necessary to reproduce the desired results. One of the largest challenges for new ecological modelers can be implementing a model into computer code. In fact, coding represents a significant barrier for entry into ecological modeling, since most ecologists have not had formal training in computer science or software development. While software packages do exist that facilitate model development, we have observed that newer modelers still struggle with developing good coding practice throughout the modeling process. During a series of agent-based modeling short-courses and full semester graduate courses, both taught in NetLogo, we identified some common challenges encountered by graduate students and environmental professionals as they learn to code an ecological model, many for the first time. We were able to categorize and provide examples of the main challenges and obstacles, which fell into three main groups that follow the steps of good modeling practice: problem scoping and conceptualization, formulation, and evaluation. We then provide guidance on how to overcome these obstacles while developing good coding and modeling practices that will result in more scientifically defensible models.

Constant Values Distribution Investigation in the C Programs Source Code

Currently, software engineering plays a key role in software development, one of the criteria for the development of which is the investigation of its factology and various scientific and practical patterns. An important aspect of this area is the logic of program execution, operating with internal data, and, in particular, constant values, the identification of patterns in which actualizes this research. The main applications of this pattern include obtaining fundamental knowledge about algorithms, creating new and expanding existing metrics for evaluating and comparing program code, developing methods for its optimization, using it in genetic programming, etc.The purpose of this article is to obtain the frequency distribution of constant values in the source code of programs in the C programming language.The essence of the presented approach is to create a method for statistical analysis of the text of the source codes of programs contained in the ExeBench dataset (which consists of a huge amount of source code of functions in the C programming language, their assembler code for various processor architectures, compilation errors and other information).The proposed method is based on the use of algorithms for lexical and syntactic analysis of source code functions, semantic definition of constant types, and conversion of the recording of programming language symbols into the corresponding numeric or string values.The method has an implementation in the form of a software tool in the Python programming language, given in the form of an intuitive pseudocode. Experiments using this prototype allowed us to obtain the desired distribution of constant values for the source code of programs in the C programming language. Analysis of the obtained results allowed us to make a number of important theoretical and practical conclusions regarding the most frequently used constants, the correspondence of the obtained distribution to the Zipf law and its proximity to the exponential function, the anomalous appearance of a number of constants in the Top 50, etc.The scientific novelty of the proposed approach lies in the fact that the distribution of constant values for the source code of programs in the C programming language is obtained for the first time.The theoretical significance consists in obtaining new fundamental knowledge regarding the features and patterns of source code constructions, which can be extended to other programming languages.The practical significance consists in applying the distribution to a wide range of tasks, including the author's genetic reverse engineering, which in itself is a qualitatively new direction.

"A Lot of Moving Parts": A Case Study of Open-Source Hardware Design Collaboration in the Thingiverse Community

Open-source is a decentralized and collaborative method of development that encourages open contribution from an extensive and undefined network of individuals. Although commonly associated with software development (OSS), the open-source model extends to hardware development, forming the basis of open-source hardware development (OSH). Compared to OSS, OSH is relatively nascent, lacking adequate tooling support from existing platforms and best practices for efficient collaboration. Taking a necessary step towards improving OSH collaboration, we conduct a detailed case study of DrawBot, a successful OSH project that remarkably fostered a long-term collaboration on Thingiverse - a platform not explicitly intended for complex collaborative design. Through analyzing comment threads and design changes over the course of the project, we found how collaboration occurred, the challenges faced, and how the DrawBot community managed to overcome these obstacles. Beyond offering a detailed account of collaboration practices and challenges, our work contributes best practices, design implications, and practical implications for OSH project maintainers, platform builders, and researchers, respectively. With these insights and our publicly available dataset, we aim to foster more effective and efficient collaborative design in OSH projects.

7-axis synchronization-integrated on-the-fly laser texturing system of freeform surface: design and development

Abstract While laser surface texturing (LST) is a promising manufacturing technique for surface functionalization, simultaneously realizing high precision and high efficiency in the LST of complex curved surface is challenging, due to continuously varied geometries of laser-matter incidence. In the present work, we propose a novel manufacturing system of 7-axis on-the-fly LST for complex curved surface, based on the integrated synchronization of 5-axis linkage motion platform with 2-axis galvanometer. Specifically, the algorithm for decomposing spatial texture trajectory on curved surface into low-frequency and high-frequency parts is established, based on which the kinematic model of synchronized 7-axis system is developed to derive the motion of each axis in both 5-axis linkage motion platform and 2-axis galvanometer simultaneously. Subsequently, the synchronized 7-axis LST system is experimentally realized, including the setup of mechanical stages integrated with optical path, the configuration of numerical control unit, and the development of processing software. Finally, case study of 7-axis on-the-fly LST of freeform aluminum surface is performed, and the advantages in terms of processing efficiency and texturing accuracy over 5-axis linkage LST are demonstrated. The correlation of reduced following errors between mechanical stages with the promoted performance of curved surface texturing by the 7-axis on-the-fly LST is further analyzed. Current work provides a feasible solution for establishing the manufacturing system for high performance LST of complex curved surface.

Code-ifying the Law: How Disciplinary Divides Afflict the Development of Legal Software

Proponents of legal automation believe that translating the law into code can improve the legal system. However, research and reporting suggest that legal software systems often contain flawed translations of the law, resulting in serious harms such as terminating children's healthcare and charging innocent people with fraud. Efforts to identify and contest these mistranslations after they arise treat the symptoms of the problem, but fail to prevent them from emerging. Meanwhile, existing recommendations to improve the development of legal software remain untested, as there is little empirical evidence about the translation process itself. In this paper, we investigate the behavior of fifteen teams---nine composed of only computer scientists and six of computer scientists and legal experts---as they attempt to translate a bankruptcy statute into software. Through an interpretative qualitative analysis, we characterize a significant epistemic divide between computer science and law and demonstrate that this divide contributes to errors, misunderstandings, and policy distortions in the development of legal software. Even when development teams included legal experts, communication breakdowns meant that the resulting tools predominantly presented incorrect legal advice and adopted inappropriately harsh legal standards. Study participants did not recognize the errors in the tools they created. We encourage policymakers and researchers to approach legal software with greater skepticism, as the disciplinary divide between computer science and law creates an endemic source of error and mistranslation in the production of legal software.

Development of Staff Evaluation Software Based on Association Matrix Methods and Data Mining Using the Streamlit Framework

This study discusses evaluating employee performance in microbiology laboratories using an association matrix implemented in web-based software with the Streamlit framework. The purpose of the research is to improve the employee performance evaluation process, which previously used conventional methods. This software is built from a sample receipt recording history data stored in a MySQL database. The initially unstructured data was processed using Python libraries such as NumPy, Matplotlib, Pandas, and Difflib to generate personnel evaluation information such as specialization, task duration, workload, and individual competencies. This software can provide a fast and accurate performance assessment according to the evaluation period. In a test with the System Usability Scale (SUS), the software scored 75.83, which was rated "good.". These results show that the software is easy to use and can improve the efficiency of employee performance evaluation. Follow-up tests with questionnaires given to 18 users showed that this system was preferable to previous conventional methods. This software helps laboratory managers evaluate employee performance effectively and efficiently.

Automated decision making in Barrett’s oesophagus: development and deployment of a natural language processing tool

Manual decisions regarding the timing of surveillance endoscopy for premalignant Barrett’s oesophagus (BO) is error-prone. This leads to inefficient resource usage and safety risks. To automate decision-making, we fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to categorize BO length (EndoBERT) and worst histopathological grade (PathBERT) on 4,831 endoscopy and 4,581 pathology reports from Guy’s and St Thomas’ Hospital (GSTT). The accuracies for EndoBERT test sets from GSTT, King’s College Hospital (KCH), and Sandwell and West Birmingham Hospitals (SWB) were 0.95, 0.86, and 0.99, respectively. Average accuracies for PathBERT were 0.93, 0.91, and 0.92, respectively. A retrospective analysis of 1640 GSTT reports revealed a 27% discrepancy between endoscopists’ decisions and model recommendations. This study underscores the development and deployment of NLP-based software in BO surveillance, demonstrating high performance at multiple sites. The analysis emphasizes the potential efficiency of automation in enhancing precision and guideline adherence in clinical decision-making.

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)

Fostering positive instruction of software development cycle through a culturally responsive pedagogy

Application/Software development, a component of ICT is an important skill needed to help address sustainability given its high societal leverage. On recognizing the complexity of understanding software development cycle and putting into consideration the technicalities involved in developing software to solve organizational problems, there is a dire need to employ innovative pedagogy into classroom learning in more creative ways to. This study investigates the effectiveness of a culturally responsive pedagogy (Culturo‑Techno‑Contextual Approach) in improving students’ meaningful learning of software development cycle. The research design was a quasi‑experimental design, 127 senior secondary two ICT students were engaged from two schools in Lagos State, Nigeria. The ANCOVA output demonstrated a statistically significant difference in the achievement [F (1,136) = 172.13; p < .05]. However, no significant difference was found in the achievement between male and female students using CTCA. The findings contribute to ongoing discourse on innovative pedagogical approaches in ICT education.