User stories are informal, non-technical descriptions of features from a user's perspective that guide collaboration and iterative development in Agile projects. However, ambiguities in user stories can lead to miscommunication among stakeholders. Design models, such as UML sequence diagrams, are essential for enhancing communication, clarifying system behavior, and improving the development process. This paper presents an automated approach for generating behavioral models specifically sequence diagrams from natural language requirements expressed as user stories. We also investigate the effectiveness of a Large Language Model (LLM) in using generative AI for this task. By applying our approach and ChatGPT to two benchmark datasets with the same set of user stories, we generated corresponding sequence diagrams for comparison. Expert evaluations in Software Engineering reveal that our approach effectively produces relevant, simplified diagrams for straightforward user stories, whereas the LLM tends to create more complex diagrams that sometimes go beyond the simplicity of the original user stories.

In empirical software engineering research there is an increased use of questionnaires and surveys to collect information from practitioners. Typically, such data is then analyzed based on overall, descriptive statistics. Even though this can capture the general trends there is a risk that the opinions of different (minority) sub-groups are lost. Here we propose the use of clustering to segment the respondents so that a more detailed analysis can be achieved. Our findings suggest that it can give a better insight about the survey population and the participants' opinions. This partitioning approach can show more precisely the extent of opinion differences between different groups. This approach also gives an opportunity for the minorities to be heard. Through the process significant new findings may also be obtained. In our example study regarding the state of testing and requirement activities in industry, we found several significant groups that showed significant opinion differences from the overall conclusion.

We introduce a family of coverage criteria, called Multi-Point Stride Coverage (MPSC). MPSC generalizes branch coverage to coverage of tuples of branches taken from the execution sequence of a program. We investigate its potential as a replacement for dataflow coverage, such as def-use coverage. We find that programs can be instrumented for MPSC easily, that the instrumentation usually incurs less overhead than that for def-use coverage, and that MPSC is comparable in usefulness to def-use in predicting test suite effectiveness. We also find that the space required to collect MPSC can be predicted from the number of branches in the program.