In this paper, we explore challenges in communication between metrics teams and stakeholders in metrics service delivery. Drawing on interviews and interactive workshops with team members and stakeholders at two different Swedish agile software development organizations, we identify interrelated challenges such as aligning expectations, prioritizing demands, providing regular feedback, and maintaining continuous dialogue, which influence team-stakeholder interaction, relationships and performance. Our study shows the importance of understanding communicative hurdles and provides suggestions for their mitigation, therefore meriting further empirical research.

Metrics teams play an increasingly important role in handling data and information in modern software development organizations; they manage their companies’ measurement programs, collect and process data, and develop and distribute information products. Metrics teams can comprise several roles, and their set-up can differ between companies, as can the metrics maturity of host organizations. These differences impact the effectiveness and quality of a team’s measurement program. Our objective was to design and evaluate a model to describe the characteristics of a mature metrics team, which efficiently designs, develops, maintains, and evolves its organization’s measurement program. We conducted an action research study on four metrics teams of four distinct companies. We designed and evaluated a domain-specific model for assessing the maturity of metrics teams – MeTeaM – and also assessed the four metrics teams per se. Our results were two-fold: the creation of the metrics team maturity model MeTeaM and a template to assess metrics teams. Our evaluation showed that the model captures the characteristics of successful metrics teams and quantifies the maturity status of both the metrics teams and their host organizations. More mature metrics teams score higher in the MeTeaM model than less mature teams. The assessment provides less mature metrics teams with valuable insights on what factors to improve. Such insights can be shared with and acted upon successfully with their organizations.

Code reviews are one of the first quality assurance tasks in continuous software integration and delivery. The goal of our work is to reduce the need for manual reviews by automatically identify which code fragments should be further reviewed manually. We conducted an action research study with two companies where we extracted code reviews and build machine learning classifiers (AdaBoost and Convolutional Neural Network– CNN). Our results show that the accuracy of recognizing code fragments that require manual review, measured with Matthews Correlation Coefficient, was 0.70 in the combination of our own feature extraction and CNN. We conclude that this way of combining automation with manual code reviews can improve the speed of reviews while providing organizations with the possibility to support knowledge transfer among the designers.

Summary Automated measurement programs are an efficient way of collecting, processing, and visualizing measures in large software development companies. The number of measurements in these programs is usually large, which is caused by a diversity of the needs of the stakeholders. In this paper, we present the application of the self‐healing concepts to assure the availability of measurements to the stakeholders without the need for effort‐intensive and costly manual interventions of the operators. We study the measurement infrastructure at one of the development units of a large infrastructure provider. In this paper, we present how the Monitor, Analyze, Plane, and Execute with Knowledge model was instantiated in a simplistic manner to reduce the need for manual intervention in the operation of the measurement systems. Based on the experiences from the 2 cases studied in this paper, we show how an evolution toward self‐healing measurement systems is done both with a dedicated failure taxonomy and with an effective straightforward handling of the most common errors in the execution. The mechanisms studied and presented in this paper show that self‐healing provides significant improvements to the operation of the measurement program and reduces the need for daily oversight by an operator for the measurement systems.