An evaluation of effort estimation supported by change impact analysis in agile software development

Abstract

In agile software development, functionality is added to the system in an incremental and iterative manner. Practitioners often rely on expert judgment to estimate the effort in this context. However, the impact of a change on the existing system can provide objective information to practitioners to arrive at an informed estimate. In this regard, we have developed a hybrid method, that utilizes change impact analysis information for improving effort estimation. We also developed an estimation model based on gradient boosted trees (GBT). In this study, we evaluate the performance and usefulness of our hybrid method with tool support and the GBT model in a live iteration at Insiders Technologies GmbH, a German software company. Additionally, the solution was also assessed for perceived usefulness and understandability in a study with graduate and post‐graduate students. The results from the industrial evaluation show that the proposed method produces more accurate estimates than only expert‐based or only model‐based estimates. Furthermore, both students and practitioners perceived the usefulness and understandability of the method positively.

Mapeamento de métricas para desempenho de projetos ágeis

“…Assim, o estudo de Tahir, Rasool e Noman (2018) apresentou que 51% dos modelos de medição são referentes a melhoria de processo de software, 60% sobre uso de especialistas em medição e experiência, 40% sobre o uso de padrões de medição e 22% sobre o uso de ferramentas automatizadas. Tanveer et al (2019) desenvolveram um método híbrido que utiliza informações de análise de impacto de mudança na estimativa de esforço e do modelo de estimação baseado em árvores impulsionadas por gradiente (AIG). A avaliação subjetiva de Tanveer et al (2019) sobre o Hybrid Effort Estimation in Agile Software Development (HyEEASe) e do Planning Poker (PK) foi sobre medir a facilidade de uso percebida, utilidade e aprendizagem.…”

A Fuzzy AHP-based approach for prioritization of cost overhead factors in agile software development

“…However, the absence of the developer's experience, prior team knowledge, and technical complexity negatively impacts on estimation accuracy. Later, Tanveer et al [20] updated their previously developed Gradient boosted tree and compared their results with their predictions-based model. The authors evaluated the usefulness and effectiveness of their proposed model with an agile team effort estimation.…”

“…In comparison to [19,20], Conoscenti et al [21] identified the underlying reasons for inaccurate estimation and incorporated software project data analytics with development teams. After identifying the causes of inaccurate estimations, the authors used an interactive visualization tool highlighting actual and estimated effort levels based on the developer's feedback.…”

Project management: openings for disruption from AI and advanced analytics

“…Although there are many published papers with increasingly sophisticated algorithms to creating finer duration estimates in the PM literature, these either assume the data are available or acknowledge that such estimation may be difficult even with data, for example where different scales are used in varied contexts (Jovanovi c et al, 2017, p. 185). However, traditional development approaches focused on input and output can address these issues, but AI and analytics may extend estimation in new directions, for example, estimating the impacts such as cost, delay or risks in evaluating change requests (Tanveer et al, 2019).…”