Notes On Writing Effective Empirical Software Engineering Papers: An Opinionated Primer
arXiv:2506.11002 · doi:10.1145/3743095.3743100
Abstract
While mastered by some, good scientific writing practices within Empirical Software Engineering (ESE) research appear to be seldom discussed and documented. Despite this, these practices are implicit or even explicit evaluation criteria of typical software engineering conferences and journals. In this pragmatic, educational-first document, we want to provide guidance to those who may feel overwhelmed or confused by writing ESE papers, but also those more experienced who still might find an opinionated collection of writing advice useful. The primary audience we had in mind for this paper were our own BSc, MSc, and PhD students, but also students of others. Our documented advice therefore reflects a subjective and personal vision of writing ESE papers. By no means do we claim to be fully objective, generalizable, or representative of the whole discipline. With that being said, writing papers in this way has worked pretty well for us so far. We hope that this guide can at least partially do the same for others.
Accepted for publication by ACM SIGSOFT Software Engineering Notes (SEN)
References in corpus (8)
- Power Hungry Processing: Watts Driving the Cost of AI Deployment?
- Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study
- Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study
- To Type or Not to Type? A Systematic Comparison of the Software Quality of JavaScript and TypeScript Applications on GitHub
- RESTRuler: Towards Automatically Identifying Violations of RESTful Design Rules in Web APIs
- Evidence Tetris in the Pixelated World of Validity Threats
- How Do Microservice API Patterns Impact Understandability? A Controlled Experiment
- High Expectations: An Observational Study of Programming and Cannabis Intoxication