Natural language (NL) is pervasive in software requirements specifications (SRSs). However, despite its popularity and widespread use, NL is highly prone to quality issues such as vagueness, ambiguity, and incompleteness. Controlled natural languages (CNLs) have been proposed as a way to prevent quality problems in requirements documents, while maintaining the flexibility to write and communicate requirements in an intuitive and universally understood manner. In collaboration with an industrial partner from the financial domain, we systematically develop and evaluate a CNL, named Rimay, intended at helping analysts write functional requirements. We rely on Grounded Theory for building Rimay and follow well-known guidelines for conducting and reporting industrial case study research. Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is intended to be general for use across information-system domains, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study. Our contributions draw on 15 representative SRSs, collectively containing 3215 NL requirements statements from the financial domain. Our evaluation shows that Rimay is expressive enough to capture, on average, 88% (405 out of 460) of the NL requirements statements in four previously unseen SRSs from the financial domain.

In many software and systems development projects, analysts specify requirements using a combination of modeling and natural language (NL). In such situations, systematic acceptance testing poses a challenge because defining the acceptance criteria (AC) to be met by the system under test has to account not only for the information in the (requirements) model but also that in the NL requirements. In other words, neither models nor NL requirements per se provide a complete picture of the information content relevant to AC. Our work in this paper is prompted by the observation that a reconciliation of the information content in NL requirements and models is necessary for obtaining precise AC. We perform such reconciliation by devising an approach that automatically extracts AC-related information from NL requirements and helps modelers enrich their model with the extracted information. An existing AC derivation technique is then applied to the model that has now been enriched by the information extracted from NL requirements.

Acceptance criteria (AC) are implementation agnostic conditions that a system must meet to be consistent with its requirements and be accepted by its stakeholders. Each acceptance criterion is typically expressed as a natural-language statement with a clear pass or fail outcome. Writing AC is a tedious and error-prone activity, especially when the requirements specifications evolve and there are different analysts and testing teams involved. Analysts and testers must iterate multiple times to ensure that AC are understandable and feasible, and accurately address the most important requirements and workflows of the system being developed. In many cases, analysts express requirements through models, along with natural language, typically in some variant of the UML. AC must then be derived by developers and testers from such models. In this paper, we bridge the gap between requirements models and AC by providing a UML-based modeling methodology and an automated solution to generate AC. We target AC in the form of Behavioral Specifications in the context of Behavioral-Driven Development (BDD), a widely used agile practice in many application domains. More specially we target the well-known Gherkin language to express AC, which then can be used to generate executable test cases. We evaluate our modeling methodology and AC generation solution through an industrial case study in the financial domain. Our results suggest that (1) our methodology is feasible to apply in practice, and (2) the additional modeling effort required by our methodology is outweighed by the benefits the methodology brings in terms of automated and systematic AC generation and improved model precision.

Industry 4.0 is one of the most prominent initiatives towards the vision of smart manufacturing to foster efficiency and synergy among suppliers, producers, and customers. It will transform the production systems into a smart, fully integrated, and optimized cyber-physical production systems (CPPS) based on technology enablers such as Internet of Things (IoT). Moreover, the availability of a wide range of data and information in the context of CPPS provides a huge possibility to create novel business opportunities. However, for these business opportunities to materialize successfully there is an evident need to have an efficient communication and clear visualization of the new strategies and the underlying operational process among all the stakeholders involved in the manufacturing value chain. In this paper, we propose a model-based approach for creating and communicating business strategies (mission, goals, and tactics) and bridging the gap between the business strategies and corresponding operational processes using Business Motivation Model (BMM) and Business Process Modeling and Notation (BPMN) respectively. Concretely, we do the following, (i) create BMM models to communicate business ideas and connect corresponding business tactics to the processes models in BPMN, (ii) simulate processes to check achievable KPIs corresponding to different strategies (modeled in BMM), and (iii) define organizational units in BMM that perform tasks modeled in BPMN. We illustrate our approach through an exemplar case study about production of plastic cups by modeling high-level strategies and their underlying processes. To prove the feasibility of our work, we used and developed the BMM and BPMN plugins based on Eclipse Papyrus framework.

Manufacturing processes are undergoing major challenges to achieve the Smart Factory vision such as to increase systematic processes reuse and improve understandability of complex structures. However, those challenges recall closely those of software processes which have been successfully targeted by model-driven engineering techniques such as domain-specific modeling languages, modeling abstractions based on view-points, model-based formal analysis and automated model transformations. The goal of this paper is to present a research agenda to integrate those techniques in manufacturing processes to achieve the Smart Factory vision. We first survey manufacturing approaches that are based on the ISA-95 and BPMN standards. Then, we propose model-driven solutions to complement those approaches.

A key problem when developing video processing software is the difficulty to test different input combinations. In this paper, we present VANE, a variability-based testing approach to derive video sequence variants. The ideas of VANE are i) to encode in a variability model what can vary within a video sequence; ii) to exploit the variability model to generate testable configurations; iii) to synthesize variants of video sequences corresponding to configurations. VANE computes T-wise covering sets while optimizing a function over attributes. Also, we present a preliminary validation of the scalability and practicality of VANE in the context of an industrial project involving the test of video processing algorithms.