[Context] In agile software development, requirements are frequently represented in lightweight artifacts such as user stories (USs). To support their validation, USs may be complemented with additional artifacts such as acceptance criteria and test cases. [Problem] Existing studies propose frameworks for US writing, or investigate high-level factors that contribute to productivity, such as teamwork and support from management. However, empirical evidence on the impact of requirements artifacts on team efficiency is scarce. [Method] Through a mixed-methods case study at a large Dutch organization, we investigate how the use of requirements artifacts affects efficiency. We collected data from eight agile teams: 1,345 USs from nineteen sprints, US quality indicators, the quantity of associated acceptance criteria and test cases, and cycle time. We statistically analyzed the existence of correlations between these variables, and we triangulated our results through interviews with product owners and scrum masters from the involved teams. [Findings] Our employed indicators for US quality do not seem to have an effect on on-time completion, with our interviewees highlighting the trade-off between investing on writing quality and short-term efficiency. We found, instead, that the existence of acceptance criteria may be a relevant factor for on-time completion, as confirmed both by statistical tests and the interviewees.

In scientific domains, definitions provide a precise description of fundamental concepts. Although the debate within the philosophy of computer science regarding the scientific nature of software engineering (SE) is inconclusive, SE researchers have laid down important steps toward treating SE as a scientific paradigm. We aim to support precise and effective communication among SE researchers and practitioners by providing a systematic process for the identification and analysis of definitions, in order to support the selection of a suitable definition for a certain use case. Inspired by methods for the planning and execution of systematic literature reviews, we construct a method that is specific for concept definition reviews (CDRs). These reviews are performed whenever a research team wishes to obtain a detailed understanding of an SE concept that may have been characterized by dozens, if not hundreds, definitions. We built our method via two design science iterations. The first one focused on the concept feature and resulted in the definitive version of the CDR method presented in this paper. We then applied the revised method to two, related concepts: quality requirement and non-functional requirement . Besides showing the applicability of the CDR method, our results include findings regarding the characteristics and evolution of the terms. The two applications of the CDR method highlight the existence and citation of hundreds of definitions, many of which are nearly (but not exactly) identical. We put forward our method for other researchers to shed light on the key terminology in other sub-fields of SE. • Concept definition review as a feasible method for studying domain terms. • The term non-functional requirement is more often defined than quality requirement. • Community largely considers non-functional and quality requirement interchangeable.

Requirements engineering and software architecture are tightly linked disciplines. The Twin Peaks model suggests that requirements and architectural components should stay aligned while the system is designed and as the level of detail increases. Unfortunately, this is hardly the case in practical settings. We surmise that a reason for the absence of conjoint evolution is that existing models, such as the Twin Peaks, do not provide concrete guidance for practitioners. We propose the Requirements Engineering for Software Architecture (RE4SA) model to assist in analyzing the alignment and the granularity of functional requirements and architectural components. After detailing the RE4SA model in notation-independent terms, we propose a concrete instance, called RE4SA-Agile, that connects common artifacts in agile development, such as user stories and features. We introduce metrics that measure the alignment between the requirements and architecture, and we define granularity smells to pinpoint situation in which the granularity of one high-level requirement or high-level component is not uniform with the norm. We show two applications of RE4SA-Agile, including the use of the metrics, to real-world case studies. Our applications of RE4SA-Agile, which were discussed with representatives from the development teams, prove to be able to pinpoint problematic situations regarding the relationship between functional requirements and architecture. RE4SA and its metrics can be seen as a first attempt to provide a concrete approach for supporting the application of the Twin Peaks model. We expect future research to apply our metrics to additional cases and to provide variants for RE4SA that support different concrete notations, and extend the perspective beyond the functional perspective of this research, similar to what we did with RE4SA-Agile in this paper.

The regulation of medical consultations for some countries, such as the Netherlands, dictates the general practitioners to prepare a detailed report for each consultation, for accountability purposes. Automatic report generation during medical consultations can simplify this time-consuming procedure. Action recognition for automatic reporting of medical actions is not a well-researched area, and there are no publicly available medical video databases. We present in this paper Video2Report, the first publicly available medical consultancy video database involving interactions between a general practitioner and one patient. After reviewing the standard medical procedures for general practitioners, we select the most important actions to record, and have an actual medical professional perform the actions and train further actors to create a resource. The actions, as well as the area of investigation during the actions are annotated separately. In this paper, we describe the collection setup, provide several action recognition baselines with OpenPose feature extraction, and make the database, evaluation protocol and all annotations publicly available. The database contains 192 sessions recorded with up to three cameras, with 332 single action clips and 119 multiple action sequences. While the dataset size is too small for end-to-end deep learning, we believe it will be useful for developing approaches to investigate doctor-patient interactions and for medical action recognition.