Online software businesses constantly face new challenges. Businesses are competing to deliver high-quality software solutions to their end users as quickly as possible. The performance of a software team in terms of software delivery needs to be measured to identify bottlenecks and understand what can be improved. This project is a case study of a software organisation that delivers online web solutions for a business. The organisation strives to follow a DevOps mindset and work data-driven by collecting data, learning from it and acting on the learnings. Our method to perform the case study was to study existing data collected by the organisation and conduct interviews with software professionals to get their insights about the software delivery performance of three software teams within the organisation. We focus on two metrics - deployment frequency and lead time for change - which measure the throughput of a software team and how often and fast it can deploy changes to production. Software organisations that adopt a DevOps approach are facing challenges with collecting data about their software delivery performance and learning from that data to improve their throughput. We aim to identify these challenges and discuss various problems that software organisations need to be aware of when measuring software delivery performance. We present the results from the interviews and collected metrics, analyse them and discuss them, as well as give suggestions on future research. We observe that there are multiple factors impacting software delivery performance and various challenges with measuring it accurately. This is related to the way of reporting data reliably and developing models to study and understand the collected data.

Software development is an area in which companies not only need to keep up with the latest technology, but they additionally need to continuously increase their productivity to stay competitive in the industry. One company currently facing these challenges is Storytel - one of the strongest players on the Swedish audiobook market - with about a fourth of all employees involved with software development, and a rapidly growing workforce. With the purpose of understanding how the Storytel Tech Department is performing, this thesis maps Storytel’s productivity defined through the Four Key Metrics - Deployment Frequency, Delivery Lead Time, Mean Time To Restore and Change Fail Rate. A classification is made into which performance category (Low, Medium, High, Elite) the Storytel Tech Department belongs to through a deep-dive into the raw system data existing at Storytel, mainly focusing on the case management system Jira. A survey of the Tech Department was conducted, to give insights into the connection between human and technical factors influencing productivity (categorized into Culture, Environment, and Process) and estimated productivity. Along with these data collections, interviews with Storytel employees were performed to gather further knowledge about the Tech Department, and to understand potential bottlenecks and obstacles. All Four Key Metrics could be determined based on raw system data, except the metric Mean Time To Restore which was complemented by survey estimates. The generalized findings of the Four Key Metrics conclude that Storytel can be minimally classified as a ‘medium’ performer. The factors, validated through factor analysis, found to have an impact on the Four Key Metrics were Generative Culture, Efficiency (Automation and Shared Responsibility) and Number of Projects. Lastly, the major bottlenecks found were related to Architecture, Automation, Time Fragmentation and Communication. The thesis contributes with interesting findings from an expanding, middle-sized, healthy company in the audiobook streaming industry - but the results can be beneficial for other software development companies to learn from as well. Performing a similar study with a greater sample size, and additionally enabling comparisons between teams, is suggested for future research.