2020/04/13 by Ralf Lämmel, Lämmel, Ralf, Alvin Kerber +3
Business, Management and Accounting · Computer Science · #Business Process Modeling and Analysis #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Software Engineering (cs.SE) #Software Engineering Research #Software System Performance and Reliability
paper · pdf · doi:10.48550/arxiv.2004.06174
openalex publication_date 2020/04/13 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Understanding what a software engineer (a developer, an incident responder, a\nproduction engineer, etc.) is working on is a challenging problem -- especially\nwhen considering the more complex software engineering workflows in\nsoftware-intensive organizations: i) engineers rely on a multitude (perhaps\nhundreds) of loosely integrated tools; ii) engineers engage in concurrent and\nrelatively long running workflows; ii) infrastructure (such as logging) is not\nfully aware of work items; iv) engineering processes (e.g., for incident\nresponse) are not explicitly modeled. In this paper, we explain the\ncorresponding 'work-item prediction challenge' on the grounds of representative\nscenarios, report on related efforts at Facebook, discuss some lessons learned,\nand review related work to call to arms to leverage, advance, and combine\ntechniques from program comprehension, mining software repositories, process\nmining, and machine learning.\n