The COVID−19 pandemic has accelerated the adoption of digital health solutions. This has presented significant challenges for software development teams to swiftly adjust to the market needs and demand. To address these challenges, product management teams have had to adapt their approach to software development, reshaping their processes to meet the demands of the pandemic. Brighsquid implemented a new task assignment process aimed at enhancing developer accountability toward the customer. To assess the impact of this change on code ownership, we conducted a code change analysis. Additionally, we surveyed 67 developers to investigate the relationship between accountability and ownership more broadly. The findings of our case study indicate that the revised assignment model not only increased the perceived sense of accountability within the production team but also improved code resilience against ownership changes. Moreover, the survey results revealed that a majority of the participating developers (67.5%) associated perceived accountability with artifact ownership.

Requirements engineering in software systems has two main aspects: (1) functional system requirements, and (2) non-functional requirements of the network/community. Despite the difference in their complexities, the two aspects are strongly coupled. Requirements engineering can be done by performing scientific analysis which requires the collection, processing, and modelling of large sets of data. This data usually originates from different data sources. The challenge is identifying the right sources and finding relationships between them to make the analysis useful. To explore the relationship between product usage and performance, as well as changes in requirements, we have utilized data related to secure health communications to gain insight into possible links. We will explore the collection and processing of different types of data to facilitate requirements engineering in this setting. We will use data from multiple sources to get the volume of requirements and bugs, volume of users and user types, as well as system utilization and service usage. The analysis allows us to investigate high-level connections between user requirements and system utilization. Moreover, it provides a method for the collection, processing, and analysis of large data during requirements engineering.

Background: Understanding and controlling the impact of change decides about the success or failure of evolving products. The problem magnifies for start-ups operating with limited resources. Their usual focus is on Minimum Viable Product (MVP's) providing specialized functionality, thus have little expense available for handling changes. Aims: Change Impact Analysis (CIA) refers to the identification of source code files impacted when implementing a change request. We extend this question to predict not only affected files, but also the effort needed for implementing the change, and the duration necessary for that. Method: This study evaluates the performance of three textual similarity techniques for CIA based on Bag of words in combination with either topic modeling or file coupling. Results: The approaches are applied on data from two industrial projects. The data comes as part of an industrial collaboration project with Brightsquid, a Canadian start-up company specializing in secure communication solutions. Performance analysis shows that combining textual similarity with file coupling improves impact prediction, resulting in Recall of 67%. Effort and duration can be predicted with 84% and 72% accuracy using textual similarity only. Conclusions: The relative effort invested into CIA for predicting impacted files can be reduced by extending its applicability to multiple dimensions which include impacted files, effort, and duration.