The state-of-practice in requirements specification: an extended interview study at 12 companies

Requirements specifcation is a core activity in the requirements engineering phase of a software development project. Researchers have contributed extensively to the feld of requirements specifcation, but the extent to which their proposals have been adopted in practice remains unclear. We gathered evidence about the state of practice in requirements specifcation by focussing on the artefacts used in this activity, the application of templates or guidelines, how requirements are structured in the specifcation document, what tools practitioners use to specify requirements, and what challenges they face. We conducted an interview-based survey study involving 24 practitioners from 12 diferent Swedish IT companies. We recorded the interviews and analysed these recordings, primarily by using qualitative methods. Natural language constitutes the main specifcation artefact but is usually accompanied by some other type of instrument. Most requirements specifcations use templates or guidelines, although they seldom follow any fxed standard. Requirements are always structured in the document according to the main functionalities of the system or to project areas or system parts. Diferent types of tools, including MS Ofce tools, are used, either individually or combined, in the compilation of requirements specifcations. We also note that challenges related to the use of natural language (dealing with ambiguity, inconsistency, and incompleteness) are the most frequent challenges that practitioners face in the compilation of requirements specifcations. These fndings are contextualized in terms of demographic factors related to the individual interviewees, the organization they are afliated with, and the project they selected to discuss during our interviews. A number of our fndings have been previously reported in related studies. These fndings show that, in spite of the large number of notations, models and tools proposed from academia for improving requirements specifcation, practitioners still mainly rely on plain natural language and general-purpose tool support. We expect more empirical studies in this area in order to better understand the reason of this low adoption of research results.This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project/funding scheme PID2020-117191RB-I00/AEI/.Peer ReviewedPostprint (published version

Continuous Development and Management of Credible Modelica Models

Modeling and simulation is increasingly used in the design process for a~wide span of applications. Rising demands and the complexity of modern products also increases the need for models and tools capable to cover areas such as virtual testing, design-space exploration or digital twins, and to provide measures of the quality of the models and the achieved results. In this article, we try to summarize the state-of-the-art and best-practice from the viewpoint of a~Modelica language user, based on the experience gained in projects in which Modelica models have been utilized in the design process. Furthermore, missing features and gaps in the used processes are identified

Why and How to Extract Conditional Statements From Natural Language Requirements

Functional requirements often describe system behavior by relating events to each other, e.g. "If the system detects an error (e_1), an error message shall be shown (e_2)". Such conditionals consist of two parts: the antecedent (see e_1) and the consequent (e_2), which convey strong, semantic information about the intended behavior of a system. Automatically extracting conditionals from texts enables several analytical disciplines and is already used for information retrieval and question answering. We found that automated conditional extraction can also provide added value to Requirements Engineering (RE) by facilitating the automatic derivation of acceptance tests from requirements. However, the potential of extracting conditionals has not yet been leveraged for RE. We are convinced that this has two principal reasons: 1) The extent, form, and complexity of conditional statements in RE artifacts is not well understood. We do not know how conditionals are formulated and logically interpreted by RE practitioners. This hinders the development of suitable approaches for extracting conditionals from RE artifacts. 2) Existing methods fail to extract conditionals from Unrestricted Natural Language (NL) in fine-grained form. That is, they do not consider the combinatorics between antecedents and consequents. They also do not allow to split them into more fine-granular text fragments (e.g., variable and condition), rendering the extracted conditionals unsuitable for RE downstream tasks such as test case derivation. This thesis contributes to both areas. In Part I, we present empirical results on the prevalence and logical interpretation of conditionals in RE artifacts. Our case study corroborates that conditionals are widely used in both traditional and agile requirements such as acceptance criteria. We found that conditionals in requirements mainly occur in explicit, marked form and may include up to three antecedents and two consequents. Hence, the extraction approach needs to understand conjunctions, disjunctions, and negations to fully capture the relation between antecedents and consequents. We also found that conditionals are a source of ambiguity and there is not just one way to interpret them formally. This affects any automated analysis that builds upon formalized requirements (e.g., inconsistency checking) and may also influence guidelines for writing requirements. Part II presents our tool-supported approach CiRA capable of detecting conditionals in NL requirements and extracting them in fine-grained form. For the detection, CiRA uses syntactically enriched BERT embeddings combined with a softmax classifier and outperforms existing methods (macro-F_1: 82%). Our experiments show that a sigmoid classifier built on RoBERTa embeddings is best suited to extract conditionals in fine-grained form (macro-F_1: 86%). We disclose our code, data sets, and trained models to facilitate replication. CiRA is available at http://www.cira.bth.se/demo/. In Part III, we highlight how the extraction of conditionals from requirements can help to create acceptance tests automatically. First, we motivate this use case in an empirical study and demonstrate that the lack of adequate acceptance tests is one of the major problems in agile testing. Second, we show how extracted conditionals can be mapped to a Cause-Effect-Graph from which test cases can be derived automatically. We demonstrate the feasibility of our approach in a case study with three industry partners. In our study, out of 578 manually created test cases, 71.8% can be generated automatically. Furthermore, our approach discovered 80 relevant test cases that were missed in manual test case design. At the end of this thesis, the reader will have an understanding of (1) the notion of conditionals in RE artifacts, (2) how to extract them in fine-grained form, and (3) the added value that the extraction of conditionals can provide to RE