Understanding What Software Engineers Are Working on -- The Work-Item Prediction Challenge
Autor: | Liane Praza, Ralf Lämmel, Alvin Kerber |
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Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science business.industry Program comprehension Multitude Process mining 020207 software engineering 02 engineering and technology Computer Science - Information Retrieval Machine Learning (cs.LG) Software Engineering (cs.SE) Computer Science - Software Engineering Workflow Software Work (electrical) 0202 electrical engineering electronic engineering information engineering Production engineering Leverage (statistics) 020201 artificial intelligence & image processing Software engineering business Information Retrieval (cs.IR) |
Zdroj: | ICPC |
DOI: | 10.48550/arxiv.2004.06174 |
Popis: | Understanding what a software engineer (a developer, an incident responder, a production engineer, etc.) is working on is a challenging problem -- especially when considering the more complex software engineering workflows in software-intensive organizations: i) engineers rely on a multitude (perhaps hundreds) of loosely integrated tools; ii) engineers engage in concurrent and relatively long running workflows; ii) infrastructure (such as logging) is not fully aware of work items; iv) engineering processes (e.g., for incident response) are not explicitly modeled. In this paper, we explain the corresponding 'work-item prediction challenge' on the grounds of representative scenarios, report on related efforts at Facebook, discuss some lessons learned, and review related work to call to arms to leverage, advance, and combine techniques from program comprehension, mining software repositories, process mining, and machine learning. Comment: This paper appears in Proceedings of 28th International Conference on Program Comprehension, ICPC 2020. The subject of the paper is covered by the first author's keynote at the same conference |
Databáze: | OpenAIRE |
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