Supporting Project Management Through Integrated Management of System and Project Knowledge

  • Barbara Paech
  • Alexander Delater
  • Tom-Michael Hesse


Software engineering is a knowledge-intensive task. Many different kinds of knowledge are created, for example, system knowledge, such as requirements, design, or code, and project knowledge, such as project plans, decisions, and work items. In this chapter, we study two kinds of project knowledge: work items and decisions. Work items document what should or has been done by whom and when. Decisions represent the solution to a decision problem. They are important to be kept in mind so that the future development will be consistent with the past. These kinds of knowledge can be implicit or explicit. Work items are typically managed explicitly in issue trackers, while decisions are mostly hidden in informal notes or in the artifacts, which result from these decisions. Rationale management research has suggested several approaches to make decisions and their rationale explicit. However, in contrast to issue trackers, which are widespread, these approaches are considered as overhead in industry. In this chapter, we argue that work items and decisions should be managed together with the system knowledge. This has several benefits for project management processes, such as project planning or monitoring, for example, with better information for the allocation of work items or risk identification. We present a vision detailing these benefits and discuss what is known in research and practice about the realization of this vision. In particular, we review existing approaches to capture work items or decisions and their links to other knowledge and discuss the empirical evidence of their benefits for an integrated system and project knowledge management in industry.


System Knowledge Software Project Project Participant Traceability Link Work Item 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially supported by the DFG (German Research Foundation) under the Priority Programme SPP1593: Design for Future—Managed Software Evolution. We thank the reviewers for their helpful comments.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Barbara Paech
    • 1
  • Alexander Delater
    • 1
  • Tom-Michael Hesse
    • 1
  1. 1.Institute of Computer ScienceUniversity of HeidelbergHeidelbergGermany

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