In Requirements Engineering, requirements elicitation aims the acquisition of information from the stakeholders of a system-to-be. An important task during elicitation is to identify and render explicit the stakeholders' implicit assumptions about the system-to-be and its environment. Purpose of doing so is to identify omissions in, and conflicts between requirements. This paper o?ers a conceptual framework for the identi?cation and documentation of default requirements that stakeholders may be using. The framework is relevant for practice, as it forms a check-list for types of questions to use during elicitation. An empirical validation is described, and guidelines for elicitation are drawn.
While previous works highlight how valuable information about context is to RE, we find only few papers proposing a structured definition of context that is adapted to RE. One of them is a paper of @cite_0 , which goes on a method for requirements analysis that aims to accounts for individual, personal goals and the effect of time and context on requirements. They suggest a list of aspects to deal with, but do -- to the best of our knowledge -- no empirical validation. RE community seems to agree on the importance of further research on the link between context and RE. Cheng and Atlee @cite_27 stress the importance of context and empirical validation of RE models as a direction for future research to accelerate the transfer of research results into RE practice.
@cite_0 @cite_27
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Abstract 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, they still do not allow for empirical generalisations. Objective To lay an empirical and externally valid 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 and completed the first run in Germany. The instrument is based on a theory in the form of a set of hypotheses inferred from our experiences and available studies. We test each hypothesis in our theory and identify further candidates to extend the theory by correlation and Grounded Theory analysis. Results In this article, we report on the design of the family of surveys, its underlying theory, and the full results obtained from Germany with participants from 58 companies. The results reveal, for example, a tendency to improve RE via internally defined qualitative methods rather than relying on normative approaches like CMMI. We also discovered various RE problems that are statistically significant in practice. For instance, we could corroborate communication flaws or moving targets as problems in practice. Our results are not yet fully representative but already give first insights into current practices and problems in RE, and they allow us to draw lessons learnt for future replications. Conclusion Our results obtained from this first run in Germany make us confident that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice. The extraction of relevant and meaningful information from multivariate or high-dimensional data is a challenging problem. One reason for this is that the number of possible representations, which might contain relevant information, grows exponentially with the amount of data dimensions. Also, not all views from a possibly large view space, are potentially relevant to a given analysis task or user. Focus+Context or Semantic Zoom Interfaces can help to some extent to efficiently search for interesting views or data segments, yet they show scalability problems for very large data sets. Accordingly, users are confronted with the problem of identifying interesting views, yet the manual exploration of the entire view space becomes ineffective or even infeasible. While certain quality metrics have been proposed recently to identify potentially interesting views, these often are defined in a heuristic way and do not take into account the application or user context. We introduce a framework for a feedback-driven view exploration, inspired by relevance feedback approaches used in Information Retrieval. Our basic idea is that users iteratively express their notion of interestingness when presented with candidate views. From that expression, a model representing the user's preferences, is trained and used to recommend further interesting view candidates. A decision support system monitors the exploration process and assesses the relevance-driven search process for convergence and stability. We present an instantiation of our framework for exploration of Scatter Plot Spaces based on visual features. We demonstrate the effectiveness of this implementation by a case study on two real-world datasets. We also discuss our framework in light of design alternatives and point out its usefulness for development of user- and context-dependent visual exploration systems. One of the main tasks of requirements engineering (RE) is the creation of a requirements document that precisely, consistently, and completely describes the functional and non-functional properties of the system to be built. At some point during the RE process, the requirements are written down using a natural language or a requirements specification language. On one hand, natural language is flexible, universal, and wide-spread. On the other hand, natural language requirements are recognized widely as being incomplete, inconsistent, and inherently ambiguous. Semi-formal and formal requirements specification techniques, such as UML [13] or SCR [5], have been proposed to overcome these deficiencies. Completeness and consistency of these specifications can be tackled to some degree mechanically by tools. However, as a recent study shows, ambiguity rarely surfaces during the development of a requirements model [10]. Thus, since a requirements specification technique enforces precision, the resulting requirements model becomes unambiguously wrong. In this paper, we show how to detect ambiguities in natural language requirements using a checklist. We distinguish between linguistic and RE-specific ambiguities. Linguistic ambiguities are those ambiguities that are usually discussed in RE textbooks such as ambiguous pronoun references. RE-specific ambiguities occur with respect to the RE context, which includes the application domain and the system domain. For example, the requirement (1) If the bank customer maintains a minimum balance in his or her checking account, there is no monthly service charge. is ambiguous not because of its linguistic representation, but because of the operational environment and application domain. In the requirements documents that we have investigated, RE-specific ambiguities account for the majority of ambiguities, while pure linguistic ambiguities played a less significant role. In this paper, we present also an approach that allows identifying types of RE-specific ambiguity from a metamodel, e.g., of a requirements specification technique. Based on the identified types of ambiguity, we have developed an improved inspection technique for natural language requirements on the basis of checklists and scenario-based reading. Interviews with stakeholders can be a useful method for identifying user needs and establishing requirements. However, interviews are also problematic. They are time consuming and may result in insufficient, irrelevant or invalid data. Our goal is to re-examine the methodology of interview design, to determine how various contextual factors affect the success of interviews in requirements engineering. We present a case study of a Web conferencing system used by a support group for spousal caregivers of people with dementia. Two sets of interviews were conducted to identify requirements for a new version of the system. Both sets of interviews had the same information elicitation goals, but each used different interview tactics. A comparison of the participants' responses to each format offers insights into the relationship between the interview context and the relative success of each interview technique for eliciting the desired information. As a result of what we learned, we propose a framework to help analysts design interviews and chose tactics based on the context of the elicitation process. We call this the contextual risk analysis framework.