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Get Started Free →Use when targeting IEEE International Conference on Software Maintenance and Evolution (ICSME) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for software maintenance.
.claude/skills/brycewang-stanford-ieee-international-conference-on-software-maintenance-and-evolution/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-19 | ✗→✓ | ▲ Improved | 156% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 75% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 79% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 157% | 0% |
IEEE International Conference on Software Maintenance and Evolution (ICSME) is a top computer-science conference venue for software maintenance, evolution, comprehension, refactoring, technical debt, and empirical SE. It rewards a maintenance/evolution paper with real repository evidence and actionable engineering insight. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.
Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.
repositories, releases, technical debt, comprehension, refactoring, migration, and developer-facing maintenance decisions.
for testing and analysis of defects; ICSE/FSE/ASE for broader SE or automation claims.
If the paper misses ICSME's bar, compare against international-conference-on-software-engineering / acm-international-conference-on-the-foundations-of-software-engineering / ieee-acm-international-conference-on-automated-software-engineering / acm-sigplan-conference-on-programming-language-design-and-implementation. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.
text[Fit] High / Medium / Low (one-line reason) [Target] IEEE International Conference on Software Maintenance and Evolution (ICSME) [Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other [Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check> [Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready [Top rejection risk] <venue-specific risk> [Re-route suggestion] <better-matched conference or journal if not a fit>
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-19 | fail→pass | 14,972 | 8,863 | -41% | 1 | 1 | 0% | 1,365 | 3,496 | +156% | 0 | 0 | — |
case-01 | fail→pass | 22,985 | 12,820 | -44% | 1 | 1 | 0% | 1,955 | 3,422 | +75% | 0 | 0 | — |
case-02 | fail→pass | 25,926 | 12,318 | -52% | 1 | 1 | 0% | 1,792 | 3,206 | +79% | 0 | 0 | — |
case-03 | fail→pass | 16,365 | 13,575 | -17% | 1 | 1 | 0% | 2,202 | 3,181 | +44% | 0 | 0 | — |
case-04 | pass→fail | 20,701 | 28,562 | +38% | 1 | 1 | 0% | 2,872 | 5,557 | +93% | 0 | 0 | — |
case-05 | pass→pass | 5,058 | 10,470 | +107% | 1 | 1 | 0% | 866 | 3,015 | +248% | 0 | 0 | — |
case-06 | pass→pass | 21,099 | 12,459 | -41% | 1 | 1 | 0% | 2,990 | 4,545 | +52% | 0 | 0 | — |
case-07 | fail→fail | 14,952 | 13,477 | -10% | 1 | 1 | 0% | 1,796 | 3,282 | +83% | 0 | 0 | — |
case-08 | pass→pass | 19,451 | 15,485 | -20% | 1 | 1 | 0% | 2,075 | 3,558 | +71% | 0 | 0 | — |
case-09 | pass→pass | 10,005 | 8,289 | -17% | 1 | 1 | 0% | 1,737 | 3,105 | +79% | 0 | 0 | — |
case-10 | pass→pass | 13,439 | 12,254 | -9% | 1 | 1 | 0% | 1,640 | 3,043 | +86% | 0 | 0 | — |
case-11 | pass→pass | 14,422 | 13,324 | -8% | 1 | 1 | 0% | 1,716 | 3,242 | +89% | 0 | 0 | — |
case-12 | pass→pass | 15,478 | 10,150 | -34% | 1 | 1 | 0% | 1,673 | 2,799 | +67% | 0 | 0 | — |
case-13 | fail→fail | 14,627 | 8,519 | -42% | 1 | 1 | 0% | 1,517 | 3,433 | +126% | 0 | 0 | — |
case-14 | fail→pass | 9,786 | 10,539 | +8% | 1 | 1 | 0% | 1,363 | 3,497 | +157% | 0 | 0 | — |
case-15 | fail→fail | 17,232 | 13,849 | -20% | 1 | 1 | 0% | 1,846 | 3,293 | +78% | 0 | 0 | — |
case-16 | pass→pass | 12,995 | 14,332 | +10% | 1 | 1 | 0% | 1,939 | 2,963 | +53% | 0 | 0 | — |
case-17 | fail→pass | 12,492 | 10,833 | -13% | 1 | 1 | 0% | 2,035 | 2,959 | +45% | 0 | 0 | — |
case-18 | fail→pass | 19,317 | 7,709 | -60% | 1 | 1 | 0% | 2,173 | 3,175 | +46% | 0 | 0 | — |
case-20 | fail→pass | 16,489 | 15,981 | -3% | 1 | 1 | 0% | 1,775 | 3,718 | +109% | 0 | 0 | — |
case-21 | pass→pass | 12,908 | 11,638 | -10% | 1 | 1 | 0% | 1,934 | 2,914 | +51% | 0 | 0 | — |
case-22 | pass→pass | 22,240 | 13,083 | -41% | 1 | 1 | 0% | 2,538 | 3,309 | +30% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +32 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.