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ARXIV:2603.26137 · SOFTWARE ENGINEERING AI · SUBMITTED 30 MAR · 21:58 UTC · FRESHNESS STALE
ARXIV:2603.26137SOFTWARE ENGINEERING AISUBMITTED 30 MAR · 21:58 UTCFRESHNESS STALEXianpeng · Sun · Haonan Sun · Tian Yu · Sheng Ma · Qincheng Zhang · +2 at arXiv
A time-consistent benchmark and methodology for evaluating repository-aware software engineering AI agents, improving prompt construction and temporal validity.
Opportunity summary
Pain A time-consistent benchmark and methodology for evaluating repository-aware software engineering AI agents, improving prompt construction and temporal validity.
Evidence 9 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A time-consistent benchmark and methodology for evaluating repository-aware software engineering AI agents, improving prompt construction and temporal validity. We present a time-consistent benchmark methodology that snapshots a repository at time T0, constructs repository-derived code…
Evaluation of repository-aware software engineering systems is often confounded by synthetic task design, prompt leakage, and temporal contamination between repository knowledge and future code changes. We present a time-consistent benchmark methodology that snapshots a…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results show that prompt construction is a first-order benchmark variable. Code availability is flagged in the production record; the public repository link still…
Software Engineering AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A time-consistent benchmark and methodology for evaluating repository-aware software engineering AI agents, improving prompt construction and temporal validity.
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10.48550/arXiv.2603.26137A time-consistent benchmark and methodology for evaluating repository-aware software engineering AI agents, improving prompt construction and temporal validity.
Abstract
Evaluation of repository-aware software engineering systems is often confounded by synthetic task design, prompt leakage, and temporal contamination between repository knowledge and future code changes. We present a time-consistent benchmark methodology that snapshots a repository at time T0, constructs repository-derived code knowledge using only artifacts available before T0, and evaluates on engineering tasks derived from pull requests merged in the future interval (T0, T1]. Each historical pull request is transformed into a natural-language task through an LLM-assisted prompt-generation pipeline, and the benchmark is formalized as a matched A/B comparison in which the same software engineering agent is evaluated with and without repository-derived code knowledge while all other variables are held constant. We also report a baseline characterization study on two open-source repositories, DragonFly and React, using three Claude-family models and four prompt granularities. Across both repositories, file-level F1 increases monotonically from minimal to guided prompts, reaching 0.8081 on DragonFly and 0.8078 on React for the strongest tested model. These results show that prompt construction is a first-order benchmark variable. More broadly, the benchmark highlights that temporal consistency and prompt control are core validity requirements for repository-aware software engineering evaluation.
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Proof status
unverified9 refs; 3 sources; 50% coverage.
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PROBLEM
A time-consistent benchmark and methodology for evaluating repository-aware software engineering AI agents, improving prompt construction and temporal validity. We present a time-consistent benchmark methodology that snapshots a repository at time T0, constructs repository-deriv...
METHOD
Evaluation of repository-aware software engineering systems is often confounded by synthetic task design, prompt leakage, and temporal contamination between repository knowledge and future code changes. We present a time-consistent benchmark methodology that snapshots a reposito...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results show that prompt construction is a first-order benchmark variable. Code availability is flagged in the production record; the public repository link still needs proof alignment.
WHY NOW
Software Engineering AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
We present a time-consistent benchmark methodology that snapshots a repository at time T0, constructs repository-derived code knowledge using only artifacts available before T0, and evaluates on engineering tasks derived from pull requests merged in the future interval (T0, T1].
This is a core methodological contribution explicitly stated in the abstract and elaborated on in the introduction.
partial
and the benchmark is formalized as a matched A/B comparison in which the same software engineering agent is evaluated with and without repository-derived code knowledge while all other variables are held constant.
This describes the experimental design for evaluating the impact of repository knowledge, as stated in the abstract.
partial
Across both repositories, file-level F1 increases monotonically from minimal to guided prompts, reaching 0.8081 on DragonFly and 0.8078 on React for the strongest tested model.
This is a specific quantitative result reported in the abstract and detailed in the results section and figures.
partial
These results show that prompt construction is a first-order benchmark variable.
This is a direct conclusion drawn from the experimental results regarding prompt granularity.
partial
More broadly, the benchmark highlights that temporal consistency and prompt control are core validity requirements for repository-aware software engineering evaluation.
This is a broader conclusion about the implications of the benchmark methodology and findings.
partial
We also report a baseline characterization study on two open-source repositories, DragonFly and React, using three Claude-family models and four prompt granularities.
The repositories used for the baseline study are explicitly listed in the abstract and the 'Category Setting' table.
partial
Task source Historical PRs merged in(𝑇 0, 𝑇1]
The source of tasks for the benchmark is clearly defined in the abstract and the 'Category Setting' table.
partial
The distribution does not simply shift upward smoothly; rather, prompt strengthening moves substantial probability mass out of the zero-performance bin and into the high-F1 and exact-match bins.
This observation is made from the F1 distribution figures for both repositories, indicating the impact of prompt quality on task solvability.
partial
We present a time-consistent benchmark methodology that snapshots a repository at time T0, constructs repository-derived code knowledge using only artifacts available before T0, and evaluates on engineering tasks derived from pull requests merged in the future interval (T0, T1].
This is a core methodological contribution explicitly described in the abstract and introduction.
partial
and the benchmark is formalized as a matched A/B comparison in which the same software engineering agent is evaluated with and without repository-derived code knowledge while all other variables are held constant.
This describes the experimental setup for evaluating the impact of repository knowledge, as stated in the abstract.
partial
Across both repositories, file-level F1 increases monotonically from minimal to guided prompts, reaching 0.8081 on DragonFly and 0.8078 on React for the strongest tested model.
This is a key result reported in the abstract and supported by figures and tables showing F1 scores across different prompt granularities.
partial
Across both repositories, file-level F1 increases monotonically from minimal to guided prompts, reaching 0.8081 on DragonFly and 0.8078 on React for the strongest tested model.
Specific numerical results are provided for the highest performing models and prompts on the tested repositories.
partial
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A time-consistent benchmark and methodology for evaluating repository-aware software engineering AI agents, improving prompt construction and temporal validity.
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