Improving requirements engineering by artefact orientation

The importance of continuously improving requirements engineering (RE) has been recognised for many years. Similar to available software process improvement approaches, most RE improvement approaches focus on a normative and solution-driven assessment of companies rather than on a problem-driven RE improvement. The approaches dictate the implementation of a one-size-fits-all reference model without doing a proper problem investigation first, whereas the notion of quality factually depends on whether RE achieves company-specific goals. The approaches furthermore propagate process areas and methods, without proper awareness of the quality in the created artefacts on which the quality of many development phases rely. Little knowledge exists about how to conduct a problem-driven RE improvement that gives attention to the improvement of the artefacts. A promising solution is to start an improvement with an empirical investigation of the RE stakeholders, goals, and artefacts in the company to identify problems while abstracting from inherently complex processes. The RE improvement is then defined and implemented in joint action research workshops with the stakeholders to validate potential solutions while again concentrating on the artefacts. In this paper, we contribute an artefact-based, problem-driven RE improvement approach that emerged from a series of completed RE improvements. We discuss lessons learnt and present first result from an ongoing empirical evaluation at a German company. Our results suggest that our approach supports process engineers in a problem-driven RE improvement, but we need deeper examination of the resulting RE company standard, which is in scope of the final evaluation

On the distinction of functional and quality requirements in practice

Requirements are often divided into functional requirements (FRs) and quality requirements (QRs). However, we still have little knowledge about to which extent this distinction makes sense from a practical perspective. In this paper, we report on a survey we conducted with 103 practitioners to explore whether and, if so, why they handle requirements labeled as FRs differently from those labeled as QRs. We additionally asked for consequences of this distinction w.r.t. the development process. Our results indicate that the development process for requirements of the two classes strongly differs (e.g., in testing). We identified a number of reasons why practitioners do (or do not) distinguish between QRs and FRs in their documentation and we analyzed both problems and benefits that arise from that. We found, for instance, that many reasons are based on expectations rather than on evidence. Those expectations are, in fact, not reflected in specific negative or positive consequences per se. It therefore seems more important that the decision whether to make an explicit distinction or not should be made consciously such that people are also aware of the risks that this distinction bears so that they may take appropriate countermeasures

Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems

[Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the Fraunhofer Project Center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated.Comment: 10 pages, 8 figures, accepted for the 39th International Conference on Software Engineering (ICSE'17

Naming the pain in requirements engineering: design of a global family of surveys and first results from Germany

Context: For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems. Although we are investigating the discipline with a plethora of empirical studies, those studies either concentrate on validating specific methods or on single companies or countries. Therefore, they allow only for limited empirical generalisations. Objective: To lay an empirical and generalisable foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner. Method: We designed a globally distributed family of surveys in joint collaborations with different researchers from different countries. The instrument is based on an initial theory inferred from available studies. As a long-term goal, the survey will be regularly replicated to manifest a clear understanding on the status quo and practical needs in RE. In this paper, we present the design of the family of surveys and first results of its start in Germany. Results: Our first results contain responses from 30 German companies. The results are not yet generalisable, but already indicate several trends and problems. For instance, a commonly stated problem respondents see in their company standards are artefacts being underrepresented, and important problems they experience in their projects are incomplete and inconsistent requirements. Conclusion: The results suggest that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice