Requirements Engineering最新文献

Assurance case development (ACD) is a novel approach for demonstrating that a system is safe for use. Assurance cases comprise various project information, including requirements and their traceability to other artifacts. A practitioners’ survey was performed to understand how requirements engineering and ACD activities currently interplay. This study aimed to identify the state of practice of ACD, the existing integration between requirements engineering and ACD, and the practitioners’ opinion on this integration. The results revealed that the interplay occurs across all requirement engineering activities and that practitioners perceive benefits such as raising safety assurance awareness, early traceability development, and early identification of assurance evidence needs. Practitioners see requirements specification and change request analysis as the most suitable activities for integrating ACD and requirements engineering.

Machine Learning (ML) algorithms are widely used in building software-intensive systems, including safety-critical ones. Unlike traditional software components, Machine-Learned Components (MLC)s, software components built using ML algorithms, learn their specifications through generalizing the common features that they find in a limited set of collected examples. While this inductive nature overcomes the limitations of programming hard-to-specify concepts, the same feature becomes problematic for verifying safety in ML-based software systems. One reason is that, due to MLCs data-driven nature, there is often no set of explicitly written and pre-defined specifications, against which the MLC can be verified. In this regard, we propose to partially specify hard-to-specify domain concepts, which MLCs tend to classify, instead of fully relying on their inductive learning ability from arbitrarily-collected datasets. In this paper, we propose a semi-automated approach to construct a multi-level semantic web to partially outline the hard-to-specify, yet crucial, domain concept “pedestrian” in automotive domain. We evaluate the applicability of the generated semantic web in two ways: first, with a reference to the web, we augment a pedestrian dataset for a missing feature, wheelchair, to show training a state-of-the-art ML-based object detector on the augmented dataset improves its accuracy in detecting pedestrians; second, we evaluate the coverage of the generated semantic web based on multiple state-of-the-art pedestrian and human datasets.

ions are significant domain terms that have assisted in requirements elicitation and modeling. To extend the assistance toward requirements validation, we present in this paper an automated approach to identifying the abstractions for supporting requirements-based testing. We select relevant Wikipedia pages to serve as a domain corpus that is independent from any specific software system. We further define five novel patterns based on part-of-speech tagging and dependency parsing, and frame our candidate abstractions in the form of pairs for better testability, where the "key" helps locate "what to test", and the "value" helps guide "how to test it" by feeding in concrete data. We evaluate our approach with six software systems in two application domains: Electronic health records and Web conferencing. The results show that our abstractions are more accurate than those generated by a state-of-the-art technique. While the initial findings indicate our abstractions' capabilities of revealing bugs and matching the environmental assumptions created manually, we articulate a new way to perform requirements-based testing by focusing on a software system's changing features. Specifically, we hypothesize that the same feature would behave differently under a pair of opposing environmental conditions and assess our abstractions' applicability to this new form of feature testing.

Crowd-based Requirements Engineering (CrowdRE) promotes the active involvement of a large number of stakeholders in RE activities. A prominent strand of CrowdRE research concerns the creation and use of online platforms for a crowd of stakeholders to formulate ideas, which serve as an additional input for requirements elicitation. Most of the reported case studies are of small size, and they analyze the size of the crowd, rather than the quality of the collected ideas. By means of an iterative design that includes three case studies conducted at two organizations, we present the CREUS method for crowd-based elicitation via user stories. Besides reporting the details of these case studies and quantitative results on the number of participants, ideas, votes, etc., a key contribution of this paper is a qualitative analysis of the elicited ideas. To analyze the quality of the user stories, we apply criteria from the Quality User Story framework, we calculate automated text readability metrics, and we check for the presence of vague words. We also study whether the user stories can be linked to software qualities, and the specificity of the ideas. Based on the results, we distill six key findings regarding CREUS and, more generally, for CrowdRE via pull feedback.