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Effective data interpretation

: Münch, Jürgen


Basili, Victor R. (Ed.); Rombach, H. Dieter (Ed.); Schneider, Kurt (Ed.); Kitchenham, Barbara (Ed.); Pfahl, Dietmar (Ed.); Selby, Richard W. (Ed.):
Empirical software engineering issues : Critical assessment and future directions. International Workshop, Dagstuhl Castle, Germany, June 26-30, 2006. Revised papers
Berlin: Springer, 2007 (Lecture Notes in Computer Science 4336)
ISBN: 3-540-71300-X
ISBN: 978-3-540-71300-5
International Workshop on Empirical Software Engineering Issues <2006, Dagstuhl>
Fraunhofer IESE ()
measurement; experimentation; software engineering; empirical study; learning organization; experience factory

Data interpretation is an essential element of mature software project management and empirical software engineering. As far as project management is concerned, data interpretation can support the assessment of the current project status and the achievement of project goals and requirements. As far as empirical studies are concerned, data interpretation can help to draw conclusions from collected data, support decision making, and contribute to better process, product, and quality models. With the increasing availability and usage of data from projects and empirical studies, effective data interpretation is gaining more importance. Essential tasks such as the data-based identification of project risks, the drawing of valid and usable conclusions from individual empirical studies, or the combination of evidence from multiple studies require sound and effective data interpretation mechanisms. This article sketches the progress made in the last years with respect to data interpretation and states needs and challenges for advanced data interpretation. In addition, selected examples for innovative data interpretation mechanisms are discussed.