Software Development Challenges Research Articles

EXPLORING AGILE EFFORT ESTIMATION ISSUES: A SYSTEMATIC LITERATURE REVIEW

Effort estimation is crucial in software development, especially in Agile projects. The 2020 Standish Group survey found that only 31% of software projects success. The success of a software development project depends on the accuracy of effort estimation. This research aims to analyze studies related to effort estimation methods in Agile software development to identify related issues. A systematic literature review by Kitchenham was conducted across Emerald, Science Direct, Scopus, SpringerLink, and IEEE databases and identified 239 relevant studies from 2018 and 2023, ultimately focusing on 40 studies about effort estimation challenges in Agile software development. The research revealed 59 issues related to various estimation methods. The main challenge in effort estimation for Agile software development is team experience and limited knowledge about the domain, which results in inaccurate estimation result. Requirements’ details, tasks complexity, and lack of data will complicate problem-solving and the prediction of the duration of completion. Reliance on expert judgment will increase the risk of bias and inaccuracy in estimates. These challenges increase the likelihood of project failure due to a mismatch between initial planning and reality as development progresses.

Requirements Engineering at a Turning Point: Key Research Trends Shaping the Future of Systems and Software Integration

Requirements Engineering (RE) is experiencing a significant transformation that requires urgent attention and creativity from industry experts to address the challenges of future systems and software development. At the core of this evolution is Requirements Engineering (RE), an essential process that defines, records, and maintains the fundamental aspects of software and systems design. With the rapid increase in complexity and interconnectedness across different industries, the issues linked to requirements engineering are becoming more pressing, significantly affecting costs, timelines, and overall performance. This article outlines current research trends in requirements engineering, focusing on emerging challenges and future pathways. We investigate advancements in RE techniques, methodologies, and tools, assessing their effects on building complex systems. Furthermore, we emphasize the growing significance of stakeholder engagement and agile practices in shaping requirements, highlighting their contributions to improving collaboration and adaptability. By evaluating the present state of practice and pinpointing critical areas of research concern, this article offers a guide for future requirements engineering studies, enabling researchers and practitioners to address the changing needs of software and systems engineering.

Integrating toyota production system for sustainability and competitive advantage in medical device software design

This research explores the integration of the Toyota Production System (TPS) and sustainability into the software design and development processes of a medium-sized medical device company dealing with resource constraints and evolving customer requirements. Through an analysis of TPS principles and how they relate to manufacturing and software development challenges, the research seeks to strengthen efficiency and competitiveness. It provides an adaptable implementation plan that fosters a competitive strategy and promotes sustainability through efficient software practices. The paper presents a roadmap for systematically integrating TPS and sustainability in alignment with international best practices, positioning the company to thrive in the expanding medical software market. This framework addresses immediate operational issues while laying the groundwork for long-term success and compliance with industry standards.

Aphasia therapy software: an investigation of the research literature and the challenges of software development

ABSTRACT Aphasia therapy software: aninvestigation of the research literature and the challenges of software development Background Digital aphasia therapy has become increasingly popular over the past years. However, not all software described in the literature appears to reach clinical practice. It is currently unclear why this is the case. Aim We had two aims. First, we aimed to create an overview of the digital aphasia therapy described and its clinical availability bysystematically searching and reviewing the research literature. Second, we aimed to investigate the experiences of researchers/developers of aphasia therapy software. Method & Procedures We conducted a systematic search and review, screening references against our inclusion criteria. Clinical availability of the software was established through reading literature, conducting internet searches, and by contacting the researchers/developers who were associated with the software. Researchers/developers of the reported software were surveyed about their experiences with software development. Responses were the matically analysed into themes and subthemes. Outcomes & Results We identified a total of 63 unique pieces of software across 125 references. The software was mainly developed for English speakers and mainly focused on word retrieval or a combination of language skills. We established clinical availability with certainty for 35 pieces of software, with the remaining software (likely) currently clinically unavailable. From the survey data, we identified five recurring themes: 1) software development can be expensive and insufficient funding hinders their clinical availability; 2) commercialisation of software is often new and daunting to researchers but essential for making software available; 3) clinicians and people with aphasia are valuable sources of feedback; 4) working with software developers can be difficult; and 5) aphasia therapy software should combine both the evidence base and current clinical need. Conclusions Aminority of aphasia therapy software described in the literature reaches clinical practice. It is clear that the development and subsequent dissemination of aphasia therapy software requires large amounts of time, energy, and funds but we conclude that this is essential for successful clinical implementation.

Implementation of Scrum in the manufacture of non-invasive blood sugar detection devices using PPG signals

This study presents the effective integration of Scrum methodology in the production process of non-invasive blood sugar testing devices using Photoplethysmography (PPG) signals. During three months, a team consisting of a Product Owner, Scrum Master, and Developer Team successfully utilized Scrum's agile structure to manage the challenges of PPG signal processing, hardware integration, and software development. The repeated sprint cycles enabled swift adjustment to new obstacles and stakeholder input, guaranteeing both effectiveness and agility in the development process. The dynamic approach facilitated both the punctual delivery of complex medical equipment and the cultivation of a culture focused on ongoing enhancement, establishing a model for the future use of agile approaches in healthcare technology. The successful implementation highlights the effectiveness of Scrum in managing the complexities of medical device development. It provides a model for improving non-invasive blood sugar detection devices and establishes agile methodologies as a key driver of innovation in healthcare technology.

Impermanent identifiers: Enhanced source code comprehension and refactoring

In response to the prevailing challenges in contemporary software development, this article introduces an innovative approach to code augmentation centered around Impermanent Identifiers. The primary goal is to enhance the software development experience by introducing dynamic identifiers that adapt to changing contexts, facilitating more efficient interactions between developers and source code, ultimately advancing comprehension, maintenance, and collaboration in software development. Additionally, this study rigorously evaluates the adoption and acceptance of Impermanent Identifiers within the software development landscape. Through a comprehensive empirical examination, we investigate how developers perceive and integrate this approach into their daily programming practices, exploring perceived benefits, potential barriers, and factors influencing its adoption. In summary, this article charts a new course for code augmentation, proposing Impermanent Identifiers as its cornerstone while assessing their feasibility and acceptance among developers. This interdisciplinary research seeks to contribute to the continuous improvement of software development practices and the progress of code augmentation technology.

Achieving Syntax Excellence: Best Practices for Organizational Success

Abstract: Syntax Level Up Organization is a methodology that aims to improve the syntactical structure of codebases, fostering improved readability, maintainability, and scalability. It advocates for the systematic arrangement of code elements to enhance comprehension and streamline development workflows. Consistent coding conventions are a fundamental principle of Syntax Level Up Organization, which minimizes confusion and optimizes collaboration. Modularization promotes code reusability and minimizes dependencies, facilitating agile development practices. The judicious use of language features and idioms is another cornerstone of Syntax Level Up Organization. Developers can craft concise and expressive code by leveraging the full spectrum of a programming language’s capabilities, such as lambda functions, list comprehensions, and pattern matching. Meaningful abstractions are also advocated for to encapsulate complex logic, enhancing code maintainability and facilitating testing. Design patterns like the Factory Method, Singleton, and Strategy serve as powerful tools for structuring codebases and promoting code reuse. Code documentation is also crucial for enhancing code comprehensibility. Clear and concise comments supplement code, elucidating design decisions, highlighting edge cases, and facilitating knowledge transfer within teams. In conclusion, Syntax Level Up Organization represents a holistic approach to code organization that transcends superficial aesthetics, focusing on enhancing code clarity, efficiency, and maintainability. By embracing consistent coding conventions, modular design principles, idiomatic language features, meaningful abstractions, and comprehensive documentation, developers can elevate the quality of their codebases and empower teams to tackle modern software development challenges with confidence and agility. IndexTerms – HTML, CSS, Javascript. , MYSQL, PHP.

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices.

One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms.

Improving with Hybrid Feature Selection in Software Defect Prediction

Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.

Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Utilizing GA with NBC, project success probability improved from 53.17 to 99.68%, with cost reductions from 0.463 to 0.403%. Similarly, GA with LR increased success rates from 55.52 to 98.99%, and costs decreased from 0.496 to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs = 0.955) in causes ranking, with no significant difference between them (t = 1.195, p = 0.240 > 0.05). The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project.

Software Estimation in the Design Stage with Statistical Models and Machine Learning: An Empirical Study

Accurate estimation of software effort and time in the software development process is a key activity to achieve the necessary product quality. However, underestimation or overestimation of effort has become a key challenge for software development. One of the main problems is the estimation with metrics from late stages, because the product must already be finished to make estimates. In this paper, the use of statistical models and machine learning approaches for software estimation are used in early stages such as software design, and a data set is presented with metric values of design artifacts with 37 software projects. As results, models for the estimation of development time and effort are proposed and validated through leave-one-out cross-validation. Further, machine learning techniques were employed in order to compare software projects estimations. Through the statistical tests, it was proven that the errors were not statistically different with the regression models for effort estimation. However, with Random Forest the best statistical results were obtained for estimating development time.

ПРОГРАМНЕ ЗАБЕЗПЕЧЕННЯ РОЗУМНОГО ГОДИННИКА ДЛЯ САМОСТІЙНИХ ЗАНЯТЬ СПОРТОМ ТА ФІТНЕСОМ

According to available statistics, the most popular smart watch among users is the Apple Watch. Today, there are more than 100 million unique users of this device, 75% of whom use it for sports. With a wide array of different sensors to track a user's physical parameters, neither Apple nor third-party developers have yet developed software to systematize all the collected data to improve an athlete's physical pa-rameters and achieve personal athletic goals. The purpose of the research is to find the possibility of improving the physical parameters of a novice athlete by means of a comprehensive analysis of his activity data collected by a smart watch and creating more personalized recommendations during training based on this data. There are many fitness apps available for the Apple Watch, each with its own unique features and features. Unfortunately, none of the analyzed applications pro-vide sufficient information regarding the correct execution of training and the collec-tion of indicators, which is a disadvantage for users who want to do sports without risks to their health. One of the main challenges of software development for smartwatches is the lack of extensive monetization opportunities. This led to the lack of interest of large companies and teams in this type of software. Small screen size and limited process-ing power compared to desktop or mobile devices are also issues. This means that developers must optimize both the user interface and the algorithms of their software product to increase the speed of the application. Another challenge is the diversity of the smartwatch market, where different devices run on different operating systems and have different hardware specifications. You have to consider the specifics and limitations of each device and platform and make sure that your apps are optimized for each of them, or focus on building your software products for only a limited num-ber of devices. Due to the lack of tools for developing applications for several platforms at once, the path of developing an application for only one platform - for WatchOS - was chosen. Also, one of the goals is to create an application that is completely autono-mous from a smartphone. For this, a simple and minimalistic interface and a simple algorithm for analyzing training data have been developed. The proposed application is focused on running. Statistics will be collected dur-ing the first few training sessions. After starting the activity, the application will start displaying all the standard information, such as pace, heart rate, activity time, dis-tance traveled. This data comes from the sensors and modules of the smart watch. When basic training statistics are collected, the app will begin to display tips on how to continue training.

A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions

Artificial Intelligence (AI), as one of the most important fields of computer science, plays a significant role in the software development life cycle process, especially in the implementation phase, where developers require considerable effort to convert software requirements and design into code. Automated Code Generation (ACG) using AI can help in this phase. Automating the code generation process is becoming increasingly popular as a solution to address various software development challenges and increase productivity. In this work, we provide a comprehensive review and discussion of traditional and AI techniques used for ACG, their challenges, and limitations. By analysing a selection of related studies, we will identify all AI methods and algorithms used for ACG, extracting the evaluation metrics and criteria such as Accuracy, Efficiency, Scalability, Correctness, Generalization, and more. These criteria will be used to perform a comparative result for AI methods used for ACG, exploring their applications, strengths, weaknesses, performance, and future applications.

Integrating Generative AI for Advancing Agile Software Development and Mitigating Project Management Challenges

Integrating Generative AI for Advancing Agile Software Development and Mitigating Project Management Challenges

SmartDelta project: Automated quality assurance and optimization across product versions and variants

Software systems are often built in increments with additional features or enhancements on top of existing products. This incremental development may result in the deterioration of certain quality aspects. In other words, the software can be considered an evolving entity emanating different quality characteristics as it gets updated over time with new features or deployed in different operational environments. Approaching software development with this mindset and awareness regarding quality evolution over time can be a key factor for the long-term success of a company in today’s highly competitive market of industrial software-intensive products. Therefore, it is important to be able to accurately analyze and determine the quality implications of each change and increment to a software system. To address this challenge, the multinational SmartDelta project develops automated solutions for the quality assessment of product deltas in a continuous engineering environment. The project provides smart analytics from development artifacts and system executions, offering insights into quality degradation or improvements across different product versions, and providing recommendations for the next builds. This paper presents the challenges in incremental software development tackled in the scope of the SmartDelta project, and the solutions that are produced and planned in the project, along with the industrial impact of the project for software-intensive industrial systems.

Role of GenAI in Automated Code Generation within DevOps Practices: Explore how Generative AI

Artificial Intelligence (AI) is a pivotal domain within computer science, profoundly influencing the software development lifecycle, particularly during the implementation phase. Here, developers grapple with the task of translating software requirements and designs into executable code. Automated Code Generation (ACG) leveraging AI emerges as a promising solution in this context. The automation of code generation processes is gaining traction as a means to tackle diverse software development challenges while boosting productivity. This paper presents a thorough review and discourse on both traditional and AI-driven techniques employed in ACG, highlighting their respective challenges and constraints. Through an examination of pertinent literature, we identify various AI methodologies and algorithms utilized in ACG, extracting evaluation metrics such as Accuracy, Efficiency, Scalability, Correctness, Generalization, among others. These metrics serve as the basis for a comparative analysis of AI-driven ACG methods, delving into their applications, strengths, weaknesses, performance metrics, and future prospects.

Model transformation and code generation using a secure business process model

Emerging daily, new devices and software-driven advancements pose challenges in software development, including errors, bugs, and evolving requirements. This leads to delays in delivery. Ensuring software security within the Software Development Life Cycle (SDLC) is crucial. To address this, the research focuses on incorporating security aspects early in the SDLC through model transformation. Platform-independent models with security attributes like Integrity, Privacy, Security Audit, non-repudiation, and authentication are generated. A template-based source code generator is utilized to create the structure of the source model. The Secure Business Process Model (SBPM) encompasses Unified Modeling Language (UML) artifacts, such as analysis level classes and sequence diagrams, enriched with security attributes derived from the activity model. Security requirements are linked to elements extracted from the source model, and structural codes with security-enabled members are produced. Automation in software development is inevitable, though not complete, as it plays a vital role in addressing these challenges and improving the security of software applications.

CharM — Evaluating a model for characterizing service-based architectures

Service-based architecture is an approach that emerged to overcome software development challenges such as difficulty to scale, low productivity, and strong dependence between elements. Microservice, an architectural style that follows this approach, offers advantages such as scalability, agility, resilience, and reuse. This architectural style has been well accepted and used in industry and has been the target of several academic studies. However, analyzing the state-of-the-art and -practice, we can notice a fuzzy limit when trying to classify and characterize the architecture of service-based systems. Furthermore, it is possible to realize that it is difficult to analyze the trade-offs to make decisions regarding the design and evolution of this kind of system. Some concrete examples of these decisions are related to how big the services should be, how they communicate, and how the data should be divided/shared. Based on this context, we developed the CharM, a model for characterizing the architecture of service-based systems that adopts microservices guidelines. To achieve this goal, we followed the guidelines of the Design Science Research in five iterations, composed of an ad-hoc literature review, discussions with experts, two case studies, and a survey. As a contribution, the CharM is an easily understandable model that helps professionals with different profiles to understand, document, and maintain the architecture of service-based systems.

A systematic decision-making framework for tackling quantum software engineering challenges

Quantum computing systems harness the power of quantum mechanics to execute computationally demanding tasks more effectively than their classical counterparts. This has led to the emergence of Quantum Software Engineering (QSE), which focuses on unlocking the full potential of quantum computing systems. As QSE gains prominence, it seeks to address the evolving challenges of quantum software development by offering comprehensive concepts, principles, and guidelines. This paper aims to identify, prioritize, and develop a systematic decision-making framework of the challenging factors associated with QSE process execution. We conducted a literature survey to identify the challenging factors associated with QSE process and mapped them into 7 core categories. Additionally, we used a questionnaire survey to collect insights from practitioners regarding these challenges. To examine the relationships between core categories of challenging factors, we applied Interpretive Structure Modeling (ISM). Lastly, we applied fuzzy TOPSIS to rank the identified challenging factors concerning to their criticality for QSE process. We have identified 22 challenging factors of QSE process and mapped them to 7 core categories. The ISM results indicate that the ‘resources’ category has the most decisive influence on the other six core categories of the identified challenging factors. Moreover, the fuzzy TOPSIS indicates that ‘complex programming’, ‘limited software libraries’, ‘maintenance complexity’, ‘lack of training and workshops’, and ‘data encoding issues’ are the highest priority challenging factor for QSE process execution. Organizations using QSE could consider the identified challenging factors and their prioritization to improve their QSE process.

Challenges of Working from Home in Software Development During Covid-19 Lockdowns

The COVID-19 pandemic in 2020/2021/2022 and the resulting lockdowns forced many companies to switch to working from home, swiftly, on a large scale, and without preparation. This situation created unique challenges for software development, where individual software professionals had to shift instantly from working together at a physical venue to working remotely from home. Our research questions focus on the challenges of software professionals who work from home due to the COVID-19 pandemic, which we studied empirically at a German bank. We conducted a case study employing a mixed methods approach. We aimed to cover both the breadth of challenges via a quantitative survey, as well as a deeper understanding of these challenges via the follow-up qualitative analysis of 15 semi-structured interviews. In this article, we present the key impediments employees faced during the crisis, as well as their similarities and differences to the known challenges in distributed software development (DSD). We also analyze the employees’ job satisfaction and how the identified challenges impact job satisfaction. In our study, we focus on challenges in communication, collaboration, tooling, and management. The findings of the study provide insights into this emerging topic of high industry relevance. At the same time, the study contributes to the existing academic research on work from home and on the COVID-19 pandemic aftermath.