Software process improvement : 12th European Conference, EuroSPI 2005, Budapest, Hungary, November 9-11, 2005 : proceedings

Assessing the Impact of System Architecture on the Success of Project Management Methodologies in Software Engineering

Aligning business and IT is crucial in the software industry, where successful software projects depend not only on technology but also on management methodology. Software implementation involves development, migration, and tailoring across architecture‐based systems. The previous studies care on measuring success or failure of project methodologies without interest in system architectures and their effect on project management phases. The wrong selection of management methodology means failure of IT firms, where there is a lack in studying the success factor of selecting suitable project management methodology. Furthermore, there is no study until now that cares on searching the relationship between system architectures and project management methodologies. This paper fills this gap by finding answers for the research question “is system architecture's type one of selection factor for methodology of software project management?” This study investigates different models that measured success of the most popular project management methodologies (waterfall, agile, scrum, Kanban, Scrumban, agile‐waterfall, and DevOps) since 2019 until Jan 2026 through all three cases of software development (customization, ETO developing, migration) for three system architectures (MSA, SOA, Monolith). This study uses descriptive statistics to study the relation between system architectures and software project management. Pearson Correlation and Paired t‐test are used to study the success of developing system architecture by management methodologies. Means and Cohen's d are also used to measure the degree of effect. The main result is that management methodology has variable significance in different cases of developing three architecture‐based systems. Selecting a system architecture is correlated and one of project management's success factors.

Robin (2015). Tacit and Explicit Knowledge in Software Development Projects: Towards a Conceptual Framework for Analysis . eKnow 2015 7th International Conference on Information, Process and Knowledge Management, 49-52

MEASURING THE IMPACT OF GENERATIVE AI ON SOFTWARE TEAM PRODUCTIVITY AND OUTPUT QUALITY IN AGILE ENVIRONMENTS

Generative artificial intelligence (GAI) is becoming more incorporated into software engineering functions like code creation, debugging, requirement analysis, testing, and sharing knowledge. This research looks at how GAI affects software teams in terms of productivity and quality of the output in Agile environments. The research design used is quantitative, cross-sectional survey type using a questionnaire prepared for this research. The data used consists of 35 responses, with 34 usable cases in analyzing 30 Likert items. The measuring instrument consists of six concepts: use of GAI, efficiency of the software team, quality of the software output, GAI in Agile, team collaboration, and communication, and overall impact perceived. The descriptive results show positive feelings about the six concepts. The values on the mean for the different concepts varied from 3.54 to 3.78 on a scale of five, with GAI being the concept that received the highest mean (M = 3.78, SD = 0.47) while productivity was the one that received the lowest (M = 3.54, SD = 0.69). The instrument has a high level of internal consistency with α = 0.799 for the entire scale of 30 items. In terms of specific items, productivity, quality, and team collaboration had acceptable reliability, whereas GAI had low internal consistency and Agile and general have the upper limit of reliability therefore, construct-level findings should be interpreted cautiously. Pearson correlation analysis showed statistically significant positive associations between overall perceived impact and software output quality (r = 0.365, p = 0.034) and team collaboration and communication (r = 0.371, p = 0.031). Productivity was positively associated with overall impact but did not reach the conventional 0.05 significance level (r = 0.312, p = 0.073). In a multiple regression model, the five dimensions explained 23.5% of the variance in overall perceived impact (R² = 0.235); however, the overall model was not statistically significant (F(5, 28) = 1.719, p = 0.163). These findings support a cautious interpretation: respondents generally perceive GAI positively, but the present small sample does not provide strong evidence for broad causal claims.

A Comprehensive Systematic Literature Review of Agile Methodology

Agile methodology, which offers collaborative, adaptable, and iterative alternatives to conventional plan-driven procedures, has become a revolutionary paradigm in project management since it was codified in the Agile Manifesto. Agile was first used in software development, but it has now spread across many different industries, demonstrating its increasing applicability in dealing with complexity, ambiguity, and quick environmental change. Given this expansion, a comprehensive synthesis of the existing body of knowledge is necessary to understand the trajectory of Agile’s development, its practical applications, and its theoretical foundations. The literature demonstrates enduring issues, such as organizational resistance, misalignment with current governance structures, and the shortcomings of traditional performance measuring tools, despite widespread implementation. As a result, hybrid models that combine Agile and conventional methods have drawn more academic and real-world interest. This paper aims to present a literature review of Agile methodologies from 2001 to 2025, systematically examining their evolution, cross-sectoral diffusion, and key adaptations. It further identifies critical gaps in the literature and outlines future research directions, particularly in relation to sustainability, emerging market contexts, and the advancement of hybrid frameworks. Through this analysis, the study aims to provide a coherent and integrated understanding of Agile’s role in managing complexity and enhancing value delivery in contemporary organizational environments.

Integration of Lean Management, Agile/Scrum, and PMBOK in Improving The UAT Process (A Case Study of Banking Co.)

Digital transformation in the Indonesian banking industry has significantly in-creased pressure on software testing processes. At Banking Co., the number of features under testing rose by 295% between 2022 and 2024, from 5,840 to 43,595 features, while the existing UAT work procedures remained unchanged. This imbalance led to systematic waste, repeated rework, and increased burnout risk, threatening both operational sustainability and human capital. This study aims to identify dominant waste types in the UAT process, formulate an integrated Lean–Agile/Scrum–PMBOK solution, and analyze its potential impact on efficiency and team sustainability. A mixed methods approach was applied through a 65-item Likert survey targeting a population of 371 Testing Analysts, yielding 52 valid responses, and in-depth interviews with three expert informants from different management levels. Waste prioritization was analyzed using the Relative Im-portance Index (RII) and Composite RII. The findings identified seven categories of waste across 12 UAT activities, with Defects (0.756), Movement (0.735), and Waiting (0.725) ranking as the most dominant. The study also revealed a significant perception gap between practitioners and management regarding Movement waste, demonstrating the importance of triangulation in identifying hidden operational problems. Based on these findings, eight improvement op-tions integrating Lean Management, Agile/Scrum, and PMBOK principles were proposed, including Shared Accountability, System Stabilization, WIP Limit, Definition of Ready, and Lean-based UAT procedures. The results indicate that eliminating systemic waste could significantly reduce burnout risk and improve long-term team sustainability.

Agile Software Development Challenges: Identification, Validation, and Prioritization Using the Analytic Hierarchy Process

Context: The Agile methodology has been prevalent in the software industry for more than two decades, marking a shift from plan-driven to market-driven approaches and introducing various challenges. While the literature identifies numerous challenges in Agile development, little attention has been given to their ranking and prioritization, which are critical for effective project management and decision making. This study fills this gap by combining empirical evidence from the literature and practitioners. Objectives: This study aims to identify and hierarchically prioritize the most recent challenges faced by Agile practitioners during product development. To achieve this, a Systematic Literature Review (SLR) was conducted using 115 published studies between 2010 and 2025 followed by empirical data collection from 30 Agile experts through semi-structured interviews conducted with practitioners from Agile companies and an online survey. This study applies Cumulative Voting (100-Dollar Test) and Multi-Criteria Decision Making (MCDM) techniques to rank and prioritize these challenges. Results: The SLR identifies several recurring Agile challenges; however, limited research has focused on their ranking and prioritization. The present study reveals new challenges, such as user interface complexities, lack of pre-development and pre-operational cost information, and lack of cost scalability at the module and feature levels. The current study identifies Inadequate Architecture (22%), Lack of Standardized Framework (18%), Communication and Coordination (16%), Poor Requirement Verification (13%), and Minimum Documentation (8%) as the most significant challenges. Conclusions: This study provides valuable insight for Agile practitioners and organizations, enabling more informed project planning, resource allocation, and strategic decision making. By focusing on the most critical challenges, teams can enhance software quality, streamline processes, and improve overall productivity in Agile environments.