Bachelor's Thesis Karla Weigelt

The contemporary business environment is characterized by a high degree of uncertainty, which is particularly triggered by the rapid technological progress and constantly changing customer needs. This is especially true in the field of software development, which is of a fast-paced and constantly changing nature. For companies, it is essential to be flexible and adaptable in order to remain successful and competitive. As the environment and technology evolved traditional methodologies, such as the waterfall approach, have become obsolete due to their limitations in flexibility and adaptability often resulting in project failure. To counteract these and other disadvantages, new approaches have been developed that compensate for these disadvantages of traditional methodologies, such as the agile methodology. Originally, agile methodologies were designed for small-scale projects to respond quicker to changes in the environment and customer requirements. The success of applying agile methodologies on a small scale has inspired many organizations to apply these in larger contexts. As a result, the application of agile methods on large-scale projects is becoming increasingly popular. Despite the benefits, applying agile practices on large scale leads to additional challenges e.g., the complexity of the project increases or maintaining oversight regarding the project’s progress becomes challenging. In order to address these challenge accurate prediction e.g., effort estimations can support the project managers to track the progress of a project and therefore deliver within budget and time. In this context, the accuracy of the predictions is of great relevance and importance since any percentage error in the overall effort estimate may lead to project failure regarding e.g., delivery time or features.

To date, several publications regarding effort estimation on the team level have been conducted, but fewer with a focus on large scale agile environments. In addition, current research lacks guidance on how to perform effort estimation in scaling agile environments. To address these research gaps this thesis aims to investigate the effort estimation process in scaling agile environments in practice, identify challenges and derive mitigation propositions. In doing so, this research follows an Action Design Research (ADR) approach to generate prescriptive design knowledge by constructing and evaluating ensemble artifacts in an organizational setting. For this purpose, we conducted a case study, including 16 interviews, on a large project of a major German network and infrastructure company, which also involved a large German software company and a consulting firm. After collecting and transcribing the data, a two-cycle approach is applied for coding the data in order to analyze it. The main findings of the case study are used to develop an artifact - a set of challenges and mitigation propositions for effort estimation in large-scale agile development. Further findings in existing research, especially on the mitigation propositions, are incorporated into the artifact. The artifact is then evaluated by the experts, and refined by incorporating the improvement suggestions and qualitative feedback are.

Research Questions

1. How is effort estimation conducted in the case organization?
2. What are challenges in effort estimation in scaling agile environments?
3. How can these challenges in effort estimation in scaling agile environments be addressed?

Sources

• Hevner, A.R., March, S.T., Park, J., and Ram, S. 2004. "Design Science in Information Systems Research," MIS Quarterly (28:1), pp. 75-105. 
• Sein, M., Henfredsson, O., Purao, S., Rossi, M., and Lindgren, R. 2011. “Action Design Research,” Management Information Systems Quarterly (35:1), pp. 37-56. 
• Usman , M., Britto, R., Damm, L., and Börstler, J. 2018. „ Effort Estimation in Large-Scale Software Development: An Industrial Case Study“. Information and Software Technology Vol. 99, pp. 21–40.
• Yin, R. K. 2014. "Case Study Research: Design and Methods". 5th ed. Los Angeles, USA: SAGE Publications.