Planning for the Unknown: Lessons Learned from Ten Months of Non-participant Exploratory Observations in the Industry

Background: During the period of one year, ING developed an approach for software analytics within an environment of a large number of software engineering teams working in a Continuous Delivery as a Service setting. Goal: Our objective is to examine what factors helped and hindered the implementation of software analytics in such an environment, in order to improve future software analytics activities. Method: We analyzed artifacts delivered by the software analytics project, and performed semi-structured interviews with 15 stakeholders. Results: We identified 16 factors that helped the implementation of software analytics, and 20 factors that hindered the project. Conclusions: Upfront defining and communicating the aims, standardization of data at an early stage, build efficient visualizations, and an empirical approach help companies to improve software analytics projects. CCS CONCEPTS• Software and its engineering → Empirical software validation;staff work together in one team that works in an agile (Scrum or Kanban) way. The idea behind this is that teams -or squads, as they are called within ING in line with terminology used at Spotify [17]develop software more quickly, be more responsive to user demand, and ultimately maximize revenue.

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“…As a result, most studies are relatively small and exploratory in nature (e.g. [11] [8] [19] [23] [27]).…”

Section: Related Workmentioning

confidence: 99%