Skills Development Guide to Empirical Software Engineering Writing

Guide to Empirical Software Engineering Writing

v20260724
fse-writing-style
A comprehensive guide for revising empirical papers submitted to top-tier Software Engineering conferences (like FSE). This framework emphasizes structuring the paper around a clear SE contribution, treating research questions as contracts, backing every claim with proportional evidence, and rigorously arguing threats to validity, rather than merely listing them. It ensures the paper maintains maximum academic rigor and focus.
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Overview

FSE Writing Style

Use this when revising the main paper. FSE papers are PACMSE journal articles read by SE empiricists, so they need a software-engineering contribution stated in the first page and evidence a reviewer trusts. The failure this skill prevents is a technically fine paper that reads like a systems demo or an ML result with an SE title glued on.

Revision rules

  • Lead with the SE contribution: the problem a practitioner recognizes, why the current state is inadequate, the contribution (technique and/or finding), the evidence, and what changes for software engineering.
  • State research questions as contracts. Each RQ should name what is measured and how it will be judged; every RQ must be answered explicitly in the results, and no result should exist without an RQ it serves.
  • Pair every claim with proportional evidence — real subjects, a fair baseline, a statistic with an effect size, or a qualitative code with agreement — not adjectives.
  • Argue threats to validity; do not recite them. Name the construct, internal, external, and conclusion threats that actually bite this study, and say what you did to bound each. A boilerplate threats paragraph is a tell that the author has not stressed their own claims.
  • Respect the page budget as a design constraint, not a formatting afterthought — the ACM template is generous but finite, and a study that only fits by shrinking threats or method is over-scoped.
  • Maintain heavy double-anonymity in self-citations, tool names, acknowledgements, funding, and the data-availability wording.

Empirical-SE paper skeleton

Section Job it must do Common failure
Intro Problem, inadequacy, contribution, evidence preview, SE payoff — first page Leads with a technology trend, not a problem
Background/Motivation Why a practitioner or the field needs this now Motivation by assertion, no grounding
Approach / Study design The technique or the RQs + protocol, reproducibly Method described too thinly to re-run
Evaluation Each RQ answered with proportional evidence Metrics that proxy for the claim rather than test it
Threats to validity The threats that bite, each bounded Generic list untethered from this study
Related work Delta-first positioning against SE literature Catalog of citations with no contrast

Sentence-level rewrites

Draft pattern FSE-safe rewrite
"Our tool significantly improves..." "detects X% more defects (95% CI ...) than on <N real projects>"
"We evaluate on a large dataset." "We evaluate on <N> projects sampled by , listed in the artifact"
"Results show our approach works well." "RQ2: developers acted on of comments (effect size ...); threats in §5.2"
"State-of-the-art performance." Claim scoped to the subjects, metrics, and regime actually tested
"The LLM understands the code." "the model's output matched in of cases"

Threats-to-validity discipline

[Construct]   does the metric measure the SE outcome you claim? (e.g. proxy for causation)
[Internal]    could something other than your technique explain the effect? (confounds, tuning)
[External]    to which projects/languages/teams does the finding generalize?
[Conclusion]  are the statistics appropriate; multiple comparisons corrected; variance reported?
-> for each that bites: state it, then state the mitigation, next to the affected result

Vignette: compressing a three-RQ study

A draft with three RQs, nine figures, and a sprawling background: keep all three RQ answers, the two figures that carry the headline findings, and a threats subsection per RQ; move secondary plots and full protocol tables to the artifact with explicit forward references; cut background to what the argument needs. The test of a good cut: a reviewer should be able to answer "what did each RQ find, and what threatens it?" from the body alone.

Output format

[Writing diagnosis] clear / under-motivated / over-claimed / evidence-mismatched / over-scoped
[First-page fix] <new framing leading with the SE contribution>
[RQ audit] <RQ -> metric -> where answered -> proportional? yes/no>
[Threats fix] <threat that bites -> mitigation to add, placed by the result>
[Anonymity edits] <tool names / self-citations / links to rewrite>
Info
Category Development
Name fse-writing-style
Version v20260724
Size 4.71KB
Updated At 2026-07-28
Language