Adam Trendowicz Livres

Software effort estimation is a key element of software project planning and management. Yet, in industrial practice, the important role of effort estimation is often underestimated and/or misunderstood. In this book, Adam Trendowicz presents the CoBRA method (an abbreviation for Cost Estimation, Benchmarking, and Risk Assessment) for estimating the effort required to successfully complete a software development project, which uniquely combines human judgment and measurement data in order to systematically create a custom-specific effort estimation model. CoBRA goes far beyond simply predicting the development effort; it supports project decision-makers in negotiating the project scope, managing project risks, benchmarking productivity, and directing improvement activities. To illustrate the method's practical use, the book reports several real-world cases where CoBRA was applied in various industrial contexts. These cases represent different estimation contexts in terms of software project environment, estimation objectives, and estimation constraints. This book is the result of a successful collaboration between the process management division of Fraunhofer IESE and many software companies in the field of software engineering technology transfer. It mainly addresses software practitioners who deal with planning and managing software development projects as part of their daily work, and is also of interest for students or courses specializing in software engineering or software project management

Effort estimation is a key factor for software project success, defined as delivering software of agreed quality and functionality within schedule and budget. Traditionally, effort estimation has been used for planning and tracking project resources. Effort estimation methods that grew upon those objectives focus on providing exact estimates and usually do not support systematic and reliable analysis of the causal effort dependencies. Moreover, existing estimation methods are typically based either on large data sets or on the extensive involvement of domain experts (human expertise), which, in practice, significantly reduces their applicability in software industry. In order to handle those problems the thesis proposes a WelCoMe method that that integrates data analysis and human judgment to extracting context-specific causal effort dependencies. When applied in the context of two industrial companies WelCoMe contributed to 17% reduction in cost of building an effort model and 50% reduction in complexity of a resulting model, while increasing its predictive performance by 43%-56%. Moreover, proposed estimation method allowed identifying crucial improvement potentials with respect to organizational measurement processes that had not been identified by domain experts.