Requirements engineering process improvement: An industrial case study

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Abstract

[Context and motivation] This paper reports the results and lessons learned of a requirements engineering improvement project conducted in a Siemens business unit. [Question/problem] In particular, the project addressed the following major problems: (i) communication gap between marketing and development, resulting in misbalance between technology-driven and market-driven requirements; (ii) limited value of monolithic requirements specifications, resulting in inconsistencies across product versions; (iii) requirements overloading, resulting in cumbersome and time consuming descoping; (iv) insufficient traceability, resulting in poor or missing impact analysis, regression testing and other traceability errors; (v) intransparent mapping between a non-hierarchical topology of problem space artifacts to hierarchically structured solution space artifacts; (vi) missing support for platform variant management and reuse, resulting in long release cycles; (vii) waterfall process, resulting in inability to effectively handle change in requirements or design. [Principal ideas/results] The paper describes the situation at the business unit before the process improvement project, gives a short overview on how the project was implemented and the techniques applied to solve the various problems the organization was facing. The paper wraps up with a comparison between the initial and the final state of the requirements engineering process in the organization and finally, a lessons learned section discusses some of the highlights and pitfalls encountered during the project. [Contribution] The paper can be used as an initial point of reference to other practitioners and organizations facing similar problems and/or involved in similar improvement projects. © 2011 Springer-Verlag Berlin Heidelberg.

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APA

Markov, G. A., Hoffmann, A., & Creighton, O. (2011). Requirements engineering process improvement: An industrial case study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6606 LNCS, pp. 34–47). https://doi.org/10.1007/978-3-642-19858-8_4

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