Context:The role of product management (PM) is key for building, implementing and managing software-intensive systems. Whereas engineering is concerned with how to build systems, PM is concerned with ‘what’ to build and ‘why’ we should build the product. The role of PM is recognized as critical for the success of any product. However, few studies explore how the role of PM is changing due to recent trends that come with digitalization and digital transformation. Objectives:Although there is prominent research on PM, few studies explore how this role is changing due to the digital transformation of the software-intensive industry. In this paper, we study how trends such as DevOps and short feedback loops, data and artificial intelligence (AI), as well as the emergence of digital ecosystems, are changing current product management practices. Methods:This study employs a qualitative approach using multi-case study research as the method. For our research, we selected five case companies in the software-intensive systems domain. Through workshop sessions, frequent meetings and interviews, we explore how DevOps and short feedback loops, data and artificial intelligence (AI), and digital ecosystems challenge current PM practices. Results:Our study yielded an in-depth understanding of how digital transformation of the software-intensive systems industry is changing current PM practices. We present empirical results from workshops and from interviews in which case company representatives share their insights on how software, data and AI impact current PM practices. Based on these results, we present a framework organized along two dimensions, i.e. a certainty dimension and an approach dimension. The framework helps structure the approaches product managers can employ to select and prioritize development of new functionality. Contributions:The contribution of this paper is a framework for ‘Strategic Digital Product Management’ (SDPM). The framework outlines nine approaches that product managers can employ to maximize the return on investment (RoI) of R&D using new digital technologies.

Context:Code review is effective and widely used, yet still time-consuming. Especially, in large-scale software systems, developers may forget to change other related functions that must be changed together (aka. co-changes). This may increase the number of review iterations and reviewing time, thus delaying the code review process. Based on our analysis of 66 projects from five open-source systems, we find that there are 16%–33% of code reviews where at least one function must be co-changed, but was not initially changed. Objectives:This study aims to propose an approach to recommend co-changed functions in the context of modern code review, which could reduce reviewing time and iterations and help developers identify functions that need to be changed together. Methods:We propose CoChangeFinder, a novel method that employs a Graph Neural Network (GNN) to recommend co-changed functions for newly submitted code changes. Then, we conduct a quantitative and qualitative evaluation of CoChangeFinder with 66 studied large-scale open-source software projects. Results:Our evaluation results show that our CoChangeFinder outperforms the state-of-the-art approach, achieving 3.44% to 40.45% for top-k accuracy, 2.00% to 26.07% for Recall@k, and 0.04 to 0.21 for mean average precision better than the baseline approach. In addition, our CoChangeFinder demonstrates the capacity to pinpoint the functions related to logic changes. Conclusion:Our CoChangeFinder outperforms the baseline approach (i.e., TARMAQ) in recommending co-changed functions during the code review process. Based on our findings, CoChangeFinder could help developers save their time and effort, reduce review iterations, and enhance the efficiency of the code review process.