Context Of Software Development Research Articles

A systematic literature review on characteristics of the front-end phase of agile software development projects and their connections to project success

ContextSoftware development of new products and services often involves a front-end phase where user needs are analysed, costs and benefits are estimated, and initial plans are created. GoalThis study aims to learn more about how the introduction of agile software development has affected practices and outcomes related to cost and benefit estimation in this front-end phase and to understand better what would improve this phase. MethodWe identified, reviewed and aggregated the results from 42 relevant research articles by searching literature databases and snowballing relevant articles. ResultsThe front-end phase of agile was found to be, on average, similar and just as comprehensive as that of non-agile software development. This may be unfortunate, given the finding that more successful agile software development is connected with less detail in cost estimation and planning-related activities. A less comprehensive front-end phase may be especially beneficial for low-risk agile software development. ConclusionThe results of this review suggest that agile principles, so far, have had a limited influence on the front-end phase. We recommend more flexibility and context-dependency in how the front-end phase of agile software development is conducted, including less comprehensive estimation and planning activities for low-risk software development contexts.

Advancing green computing: Practices, strategies, and impact in modern software development for environmental sustainability

Advancing Green Computing: Practices, Strategies, and Impact in Modern Software Development for Environmental Sustainability explores the evolving landscape of green computing within the realm of software development, emphasizing the imperative for environmentally sustainable practices. In response to escalating environmental concerns, the computing industry is undergoing a paradigm shift towards reducing its carbon footprint and mitigating ecological impacts. This shift is particularly crucial in software development, given its pervasive influence on technological ecosystems. The review delves into the multifaceted dimensions of green computing, elucidating various practices and strategies that are instrumental in fostering environmental sustainability. From optimizing code efficiency to embracing energy-efficient computing architectures, the review underscores the diverse approaches available to software developers in minimizing resource consumption and carbon emissions. Furthermore, it examines the broader ramifications of these practices, emphasizing their potential to reshape the software industry's ecological footprint and contribute to global efforts for environmental conservation. Moreover, the review highlights the symbiotic relationship between green computing and modern software development methodologies. It elucidates how principles such as agile development and DevOps can be synergistically integrated with green computing practices to enhance sustainability throughout the software development lifecycle. By adopting an interdisciplinary approach that integrates environmental considerations into software design, development, and deployment processes, organizations can catalyze transformative changes towards a greener computing ecosystem. The review also investigates the tangible impact of green computing practices on environmental sustainability metrics. Through case studies and empirical analyses, it showcases the efficacy of various strategies in reducing energy consumption, carbon emissions, and electronic waste generation. Additionally, it discusses the economic and societal benefits accrued from adopting environmentally sustainable practices, ranging from cost savings to enhanced corporate social responsibility. Advancing Green Computing: Practices, Strategies, and Impact in Modern Software Development for Environmental Sustainability provides a comprehensive overview of the evolving landscape of green computing within the context of software development. It elucidates the myriad opportunities and challenges associated with fostering environmental sustainability in the computing industry and underscores the transformative potential of integrating green computing principles into modern software development practices.

Assessing the “Systemic Barriers” Affecting Lean Implementation: A Comparison Between Manufacturing and Lean Software Development Contexts

This study attempts to understand the systemic nature of barriers affecting lean implementation in two highly contrasting contexts, i.e., manufacturing and software development. The impact of barriers and their interrelationships is explained using the theoretical lens of organizational change, including dynamics of motivational contagion, resistance, and readiness to change. Furthermore, the varying impact of barriers in manufacturing and service contexts is posited using the service-dominant logic, which is subsequently examined by analyzing barrier impacts in a manufacturing firm and a software development firm in India. Lean implementation barriers common to multiple industry sectors were identified from extant literature and then subjected to graph theory (GT) methodology to quantify the causal interactions among these common barriers. Relevant data were collected from team leaders in Indian manufacturing and software development firms tasked with lean implementation in their organizations. A network of relationships was obtained using the GT approach for major barrier groups and associated subbarriers. The findings highlighted the varying effect of these barriers in the two highly contrasting contexts. This study extends the understanding of the systemic effect of lean implementation barriers, which is a relatively novel perspective.

Implementation of Waterfall Method in Model Development to Improve Learning Quality of Computer Network Courses

This research aims to improve the learning quality of Computer Network course through the implementation of Waterfall method in the development of learning model. Waterfall method, with its focus on systematic and sequential approach in software development, is adapted to design and implement effective learning structure. This study uses qualitative research design with data collection through observation, interview, and documentation. Data analysis was conducted using content analysis method to evaluate the effectiveness of Waterfall-based learning model implementation. The results show that the implementation of Waterfall method facilitates structured planning, systematic development of learning materials, and continuous evaluation, which overall contribute to the improvement of learning quality. The developed learning model encourages students' active participation and improves the understanding of key concepts in Computer Networking. This research confirms that the Waterfall method can be effectively used outside the context of software development, particularly in improving the quality of learning in the academic field.

Transpiler-Based Architecture Design Model for Back-End Layers in Software Development

The utilization of software architectures and designs is widespread in software development, offering conceptual frameworks to address recurring challenges. A transpiler is a tool that automatically converts source code from one high-level programming language to another, ensuring algorithmic equivalence. This study introduces an innovative software architecture design model that integrates transpilers into the back-end layer, enabling the automatic transformation of business logic and back-end components from a single source code (the coding artifact) into diverse equivalent versions using distinct programming languages (the automatically produced code). This work encompasses both abstract and detailed design aspects, covering the proposal, automated processes, layered design, development environment, nest implementations, and cross-cutting components. In addition, it defines the main target audiences, discusses pros and cons, examines their relationships with prevalent design paradigms, addresses considerations about compatibility and debugging, and emphasizes the pivotal role of the transpiler. An empirical experiment involving the practical application of this model was conducted by implementing a collaborative to-do list application. This paper comprehensively outlines the relevant methodological approach, strategic planning, precise execution, observed outcomes, and insightful reflections while underscoring the the model’s pragmatic viability and highlighting its relevance across various software development contexts. Our contribution aims to enrich the field of software architecture design by introducing a new way of designing multi-programming-language software.

A Perspective on Sustainable Computational Chemistry Software Development and Integration.

The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.

Artificial Intelligence-Based Tools in Software Development Processes: Application of ChatGPT

Software development processes are continuously evolving and rapidly transforming alongside the rapid changes in technology. Recently, innovations in the field of Artificial Intelligence (AI) have led to significant changes in software development practices. AI tools can greatly enhance traditional software development processes by offering developers the ability to create projects more intelligently, swiftly, and effectively. These tools can be employed in various tasks, such as code generation, test automation, error analysis, and performance improvements. Particularly, ChatGPT, an AI-based language model that has had a profound impact on almost every domain, can assist software developers in writing code faster and in a more natural language manner. In this research article, essential information about the usage of ChatGPT in the software development process is presented. To evaluate some capabilities of ChatGPT in the software development context, applications were performed on a software project. For this purpose, a software development process was constructed based on the responses provided by ChatGPT. Various questions related to software development processes were formulated, and the responses generated by GPT were evaluated. The obtained results indicated that ChatGPT exhibited excellent performance in the software development process. Based on these findings, it was observed that AI-based models like ChatGPT could be effectively utilized as assisting tools in software development processes, accelerating traditional workflows. Furthermore, AI-based tools can automate testing processes, enhancing software quality while saving time and effort.

An empirical investigation on the benefits of gamification in communication within university development teams

AbstractThe field of software management recognizes communication as a fundamental aspect, which has garnered significant attention in recent years. In response, scholars have advocated for the implementation of innovative pedagogical approaches to foster students' engagement in communication. One such approach is the utilization of gamification, the integration of game design elements into nongame contexts, which has been demonstrated to be an effective teaching technique across various fields of education. The aim of this study was to investigate the impact of gamification on in‐team communication quality within the context of software development. To this end, an experimental design was employed with four teams of third‐year students enrolled in software development courses. The results of the study indicate that the introduction of gamification significantly enhances communication quality among the students. Furthermore, our findings indicate that a specific gamification strategy, feedback, plays a crucial role in guiding the students toward desired communication behaviors. On the basis of the results of this study, it can be concluded that gamification presents a promising approach to enhance communication quality within software development teams.

The dynamic versus the stable team: The unspoken question in large‐scale agile development

SummaryThe importance of the team, its internal dynamics, and its performance are widely recognized within the software engineering community. While popular frameworks identify wholeness, stability over time, and smallness as important factors, they offer little guidance on how to form teams that achieve these three characteristics. The objective of this study is to investigate how these team characteristics interact in large‐scale software development contexts, particularly focusing on the impact of stable and dynamic teaming approaches. This was done through a multivocal study of literature, followed by individual semi‐structured interviews with 19 engineers from two companies and validation workshops with an additional two companies from unrelated industry segments. The study results show that the question of stable versus dynamic approaches to forming software engineering teams is largely unaddressed in industry, with stable teams representing a habitual default option. Meanwhile, both stable and dynamic teams clearly have respective strengths and weaknesses, calling for careful consideration of the most suitable approach in any given situation. To support such consideration, this paper presents a model of how team stability, wholeness, and smallness interact. This model is found relevant, accurate, generalizable, and useful by practitioners.

Open Source Usability and User Experience

The purpose of this article is to highlight the challenges of user-centered and usability processes and methods in a open source software development context as well as to provide some answers for these challenges.

Social Sustainability Approaches for a Sustainable Software Product

Sustainability is the use of existing resources without compromising them for future generations. Resources must be guaranteed in the environmental, economic, and social dimensions. Sustainable Software Engineering is an emerging research field aiming to minimize software development's negative impacts on society. Furthermore, software sustainability can be defined as a way of keeping something running at a certain level of quality in relation to these dimensions. From the social dimension perspective, it is necessary to confirm software's trade-offs, benefits, and impacts on society since it lacks empirical evidence of its achievements in software. In this research thesis, we will explore how the literature addresses the aspect of social sustainability during software development and understand how social sustainability approaches can be integrated into the context of agile software development. In the end, we expect to have a set of guidelines, activities, and practices that can be adopted by agile teams.

A broad approach to expert detection using syntactic and semantic social networks analysis in the context of Global Software Development

Social network analysis has been widely used in different application contexts. For example, in Global Software Development, where multiple developers with diverse skills and knowledge are involved, the use of social networking models helps to understand how these developers collaborate. Finding experts who can help address critical elements or issues in a project is a challenging and critical task. It is especially true in the context of Global Software Development projects, where developers with specific skills and knowledge often need to be identified. In this sense, searching for essential members is a valuable task, as they are fundamental to the evolution of the network. This article proposes a broad solution for syntactic and semantic analysis in social networks in the Global Software Development context. In this solution, we define a model for the social network capable of capturing collaboration between developers, incorporate strategies for temporal analysis of the network, explore the network using machine learning algorithms, and propose an ontology to enrich the data semantically. We conducted three case studies using data extracted from GitHub to evaluate the proposed approach. The case studies provide evidence that our proposed method can identify specialists, highlighting their expertise and importance to the evolution of the social network.

The role of knowledge-based resources in Agile Software Development contexts

The software value chain is knowledge-based since it is highly dependant on people. Consequently, a lack of practice in managing knowledge as a resource may jeopardise its application in software development. Knowledge-Based Resources (KBRs) relate to employees’ intangible knowledge that is deemed to be valuable to a company’s competitive advantage. In this study, we apply a grounded theory approach to examine the role of KBRs in Agile Software Development (ASD). To this aim, we collected data from 18 practitioners from five companies. We develop the Knowledge-Push theory, which explains how KBRs boost the need for change in ASD. Our results show that the practitioners who participated in the study utilise, as primary strategies, task planning, resource management, and social collaboration. These strategies are implemented through the team environment and settings and incorporate an ability to codify and transmit knowledge. However, this process of codification is non-systematic, which consequently introduces inefficiency in the domain of knowledge resource utilisation, resulting in potential knowledge waste. This inefficiency can generate negative implications for software development, including meaningless searches in databases, frustration because of recurrent problems, the unnecessary redesign of solutions, and a lack of awareness of knowledge sources.

HYBRID MULTI-CRITERIA DECISION MAKING MODEL FOR CUSTOMER KNOWLEDGE MANAGEMENT IN SOFTWARE INDUSTRY

In this study, the enablers of Customer Knowledge Management (CKM) software companies are investigated. Consequently, the conceptual CKM enabler model is developed by extracting “organizational”, “technological” and “human” factors from the literature. Then, a Multi-Criteria Decision-Making (MCDM) approach is applied to identify the dependence relationships of factors by using the Analytic Network Processes (ANP) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. The inter-dependencies significance among the factors is evaluated by DEMATEL. ANP technique is used in order to clarify the relative significance of the factors and identify the weight and priority. The results show that the “organization” dimension is the most considerable aspect of enhancing CKM in software development. The “organization” aspect has more interaction with other aspects and activates the “technology” and “human” factors. In addition, “technology” is the second dimension that influences “human” and “organization”. Due to the importance of CKM for organizations, this research provides a significant contribution in the context of CKM by extracting and ranking the CKM antecedent factors for enterprise software development context.

Spatial and Temporal Audience Behavior of Scrum Practitioners Around Semi-Public Ambient Displays

Exploring spatial and temporal audience behavior around ambient displays is an important area of HCI research. It aids in, for instance, understanding better user appropriation in natural environments. However, there are only a few tools to capture said behavior, and simultaneously, little knowledge of the space around ambient display installations exists. In this research, we report on audience behavior observed during an in-the-wild study where we deployed our custom Ambient Surfaces solution in a professional, large-scale agile software development context for circa 5 years. Across 18 weeks in 2017, we collected skeletal data with two Microsoft Kinect v2 cameras resulting in behavior information of more than 30,000 passersby. Our results indicate, among others, that users did show the highest levels of engagement at quite some distance to the Ambient Surfaces and that people engaging in direct interaction did so rather purposely. Ultimately, this article encapsulates our research’s originality in four contributions including an approach to separate passersby from real users and an in-depth exploration of skeletal data. With the tools and methods illustrated, we hope to demonstrate manifold insights for future research on audience behavior tracking.

Agile Work Practices: Scale Development and Validation

Building on team effectiveness and work design perspectives, the present study develops a novel Agile Work Practices Instrument (AWPI). This instrument measures the most common agile practices and further distinguishes them along the taskwork-teamwork continuum. Using data of 269 teams, we establish the psychometric properties and construct validity of the AWPI. Tests of measurement models and nomological networks supported the distinction between agile taskwork and agile teamwork. As hypothesized, agile taskwork related uniquely to emergent taskwork planning and team autonomy, while agile teamwork related uniquely to emergent teamwork planning and feedback. The AWPI sets the stage for more empirical research on agile working, beyond the software development context, where most agile concepts originated. By integrating team effectiveness and work design perspectives, we aim to clarify the meaning of the term “agile” for management researchers and practitioners.

Detecting topic-based communities in social networks: A study in a real software development network

In social network analysis, a key issue is the detection of meaningful communities. This problem consists of finding groups of people who are both connected and semantically aligned. In the software development context, identifying communities considering both collaborations between developers and their skills can help to address critical elements or issues in a project. However, a large amount of data and the lack of data structure make it difficult to analyze these networks’ content. In this paper, we propose a framework for detecting overlapping semantic communities and their influential members. We also propose an ontology to extract topics of interest through tag enrichment in a Q&A forum. An evaluation was conducted in a large network of software developers built with Stack Overflow’s data, showing that the proposed framework and ontology can find real communities of developers. The evaluation indicates that their members are semantic aligned and still active in the detected topics of interest, and the quantitative analysis showed that the detected communities have high internal connectivity.

Clones in deep learning code: what, where, and why?

Deep Learning applications are becoming increasingly popular worldwide. Developers of deep learning systems like in every other context of software development strive to write more efficient code in terms of performance, complexity, and maintenance. The continuous evolution of deep learning systems imposing tighter development timelines and their increasing complexity may result in bad design decisions by the developers. Besides, due to the use of common frameworks and repetitive implementation of similar tasks, deep learning developers are likely to use the copy-paste practice leading to clones in deep learning code. Code clone is considered to be a bad software development practice since developers can inadvertently fail to properly propagate changes to all clones fragments during a maintenance activity. However, to the best of our knowledge, no study has investigated code cloning practices in deep learning development. The majority of research on deep learning systems mostly focusing on improving the dependability of the models. Given the negative impacts of clones on software quality reported in the studies on traditional systems and the inherent complexity of maintaining deep learning systems (e.g., bug fixing), it is very important to understand the characteristics and potential impacts of code clones on deep learning systems. This paper examines the frequency, distribution, and impacts of code clones and the code cloning practices in deep learning systems. To accomplish this, we use the NiCad clone detection tool to detect clones from 59 Python, 14 C#, and 6 Java based deep learning systems and an equal number of traditional software systems. We then analyze the comparative frequency and distribution of code clones in deep learning systems and the traditional ones. Further, we study the distribution of the detected code clones by applying a location based taxonomy. In addition, we study the correlation between bugs and code clones to assess the impacts of clones on the quality of the studied systems. Finally, we introduce a code clone taxonomy related to deep learning programs based on 6 DL software systems (from 59 DL systems) and identify the deep learning system development phases in which cloning has the highest risk of faults. Our results show that code cloning is a frequent practice in deep learning systems and that deep learning developers often clone code from files contain in distant repositories in the system. In addition, we found that code cloning occurs more frequently during DL model construction, model training, and data pre-processing. And that hyperparameters setting is the phase of deep learning model construction during which cloning is the riskiest, since it often leads to faults.

When should we (not) use the mean magnitude of relative error (MMRE) as an error measure in software development effort estimation?

The mean magnitude of relative error (MMRE) is an error measure frequently used to evaluate and compare the estimation performance of prediction models and software professionals. This paper examines conditions for proper use of MMRE in effort estimation contexts. We apply research on scoring functions to identify the type of estimates that minimizes the expected value of the MMRE. We show that the MMRE is a proper error measure for estimates of the most likely (mode) effort, but not for estimates of the median or mean effort, provided that the effort usage is approximately log-normally distributed, which we argue is a reasonable assumption in many software development contexts. The relevance of the findings is demonstrated on real-world software development data. MMRE is not a proper measure of the accuracy of estimates of the median or mean effort, but may be used for the accuracy evaluation of estimates of most likely effort.

A teamwork effectiveness model for agile software development

Teamwork is crucial in software development, particularly in agile development teams which are cross-functional and where team members work intensively together to develop a cohesive software solution. Effective teamwork is not easy; prior studies indicate challenges with communication, learning, prioritization, and leadership. Nevertheless, there is much advice available for teams, from agile methods, practitioner literature, and general studies on teamwork to a growing body of empirical studies on teamwork in the specific context of agile software development. This article presents the agile teamwork effectiveness model (ATEM) for colocated agile development teams. The model is based on evidence from focus groups, case studies, and multi-vocal literature and is a revision of a general team effectiveness model. Our model of agile teamwork effectiveness is composed of shared leadership, team orientation, redundancy, adaptability, and peer feedback. Coordinating mechanisms are needed to facilitate these components. The coordinating mechanisms are shared mental models, communication, and mutual trust. We critically examine the model and discuss extensions for very small, multi-team, distributed, and safety-critical development contexts. The model is intended for researchers, team members, coaches, and leaders in the agile community.