PaperlayerView exact revision on arXiv

Source-linked AI summary

Process-Level Trajectory Evaluation for Environment Configuration in Software Engineering Agents

Jiayi Kuang, Yinghui Li, Xin Zhang, Yangning Li, Di Yin, Xing Sun, Ying Shen, Philip S. Yu

arXiv:2510.25694v1cs.SEcs.AIcs.CL

TL;DR

Environment configuration is a difficult, labor-intensive step in software engineering, and existing benchmarks chiefly measure end-to-end outcomes rather than capabilities along the setup process. EnConda-Bench constructs tasks from erroneous READMEs and evaluates both process-level behavior and executability. Its evaluations find that agents can localize errors but often struggle to turn feedback into effective repairs.

  • Problem

    Environment configuration requires substantial manual effort, and end-to-end benchmarks obscure failure stages and missing capabilities while high-quality evaluation data is scarce.

    Source: §1 · p. 1

  • Method

    EnConda-Bench evaluates planning, error diagnosis, feedback-driven repair, and configuration actions using automatically constructed tasks with realistic README errors.

    Source: §1 · p. 2, §3.2 · p. 6, §5 · p. 10

  • Results

    Agents show basic error localization and classification but struggle with interaction and feedback use, often producing ineffective repairs.

    Source: §1 · p. 3, §5 · p. 11

  • Takeaways & Limitations

    The authors identify strengthening agents’ interaction with the environment as an important direction for improving repair actions.

    Source: §5 · p. 11

Abstract

from arXiv · show

Large language model-based agents show promise for software engineering, but environment configuration remains a bottleneck due to heavy manual effort and scarce large-scale, high-quality datasets. Existing benchmarks assess only end-to-end build/test success, obscuring where and why agents succeed or fail. We introduce the Environment Configuration Diagnosis Benchmark, Enconda-bench, which provides process-level trajectory assessment of fine-grained agent capabilities during environment setup-planning, perception-driven error diagnosis, feedback-driven repair, and action to execute final environment configuration. Our task instances are automatically constructed by injecting realistic README errors and are validated in Docker for scalable, high-quality evaluation. Enconda-bench combines process-level analysis with end-to-end executability to enable capability assessments beyond aggregate success rates. Evaluations across state-of-the-art LLMs and agent frameworks show that while agents can localize errors, they struggle to translate feedback into effective corrections, limiting end-to-end performance. To our knowledge, Enconda-bench is the first framework to provide process-level internal capability assessment for environment configuration, offering actionable insights for improving software engineering agents.

1 Introduction

Environment configuration is a critical, labor-intensive first step in software engineering, while existing benchmarks and scarce high-quality data limit detailed assessment. EnConda-Bench evaluates agents along the configuration trajectory and its evaluations find that repair remains a key weakness.

1 Introduction

  • End-to-end build-and-test benchmarks obscure failure stages and missing agent capabilities, while expert effort makes high-quality evaluation data difficult to scale.Correctly buildable repositories are scarce, and selecting and annotating them requires expert effort.Source: §1 · pp. 1–2
  • The proposed process-level evaluation assesses planning, error-cause localization, feedback-based repair analysis, and actions that complete configuration and pass evaluation.It is intended to provide references for improving agent capabilities and subsequent studies.Source: §1 · p. 2
  • The benchmark tests agents on READMEs with injected errors, revealing which error types they detect or repair more readily.The design follows README setup steps and observes agent responses to induced failures.Source: §1 · p. 2
  • EnConda-Bench pairs trajectory-based capability assessment with an automated data pipeline that reduces manual labor and supplies large-scale training data.The pipeline selects repositories, edits environment READMEs with common errors, annotates them, and validates and filters the resulting instances.Source: §1 · pp. 2–3
  • Agents show basic error localization and classification but limited environmental interaction and feedback use, often producing ineffective repairs.The authors identify translating feedback into effective corrective actions as a persistent difficulty.Source: §1 · pp. 2–3

2 Related Work

Prior environment-setup approaches range from dependency heuristics to tool-augmented and specialized agents, while setup benchmarks largely judge end-to-end outcomes. EnConda-Bench is positioned as adding process-level trajectory evaluation to that landscape.

Agent Methods

  • Environment-setup agents have progressed from dependency heuristics to tool-augmented and specialized interaction agents, but setup remains fragile around toolchains and long decision chains.Earlier heuristics can miss system packages, version pinning, and platform heterogeneity; specialized systems differ in language coverage and evaluation design.Source: §2 · p. 3

Environment Configuration Benchmarks

  • Setup benchmarks assess repositories through tests, build success, or test-result deviations, but most still provide limited visibility into where and why configuration fails.The surveyed benchmarks include Python-only and multilingual collections, while their evaluations remain largely end-to-end.Source: §2 · p. 3
  • Figure 2 is described as an example of the overall workflow for the process-level environment-configuration task.The caption identifies it as a workflow example without specifying further stages.Source: Figure 2 · p. 4

3 EnConda-Bench

EnConda-Bench evaluates environment-configuration agents across their planning, diagnosis, repair, and execution trajectory. It builds tasks by injecting and validating README errors, then assesses both process-level capabilities and end-to-end executability.

3.1 Task Definition and Workflow

  • Agents plan from repository READMEs, diagnose errors, propose repairs, and generate setup scripts, enabling evaluation of capabilities along the configuration trajectory.The workflow extracts error judgments, repair commands, and the final shell script from agent trajectories.Source: §3.1 · pp. 4–5

3.2 Data Construction

  • Starting from executable READMEs, the pipeline injects two errors per initial case, validates and filters them, then diversifies to 4,201 READMEs with 9,471 errors.The LLM filter and human reviewers agree on 98.5% of judgments.Source: §3.2 · p. 6

3.3 Dataset Statistics and Data Evaluation

  • 4,201 READMEs from 323 repositories average 13 per repository; five standard error categories each contain around 1,600 instances, while “Other” has 312.Human-rated difficulty distributions and average scores closely match real-world benchmark instances.Source: §3.3 · pp. 6–7, Figure 4 · p. 6, Table 2 · p. 7

3.4 Evaluation Suite Design

  • The evaluation suite scores predicted error types, descriptions, and fixes against gold answers, then runs setup scripts in Docker to test build and unit-test success.A run passes only when the environment builds, tests execute correctly, and the process exits normally.Source: §3.1 · p. 5, §3.4 · p. 7

4 Experiments

Across evaluated models and agent frameworks, stronger error diagnosis does not consistently translate into effective fixes or successful environment setup. Error-type patterns, token use, and case studies reveal additional weaknesses in diagnosis and execution.

4.1 Baselines

  • The evaluation covers zero-shot foundation models, code-agent frameworks, and environment-setup agents, including Repo2Run and INSTALLAMATIC.The evaluated foundation models include GPT-4.1, Claude-4-sonnet-20250514, Gemini2.5-Pro, and DeepSeek-V3-0324.Source: §4.1 · p. 8

4.3 Error Type judgment Analysis

  • Predicted error counts exceed ground truth, with overprediction concentrated in E1 and the catch-all E8; DeepSeek-V3 under-detects E6.Overusing E8 creates false positives for that class and reduces recall for concrete error types.Source: §4.3 · pp. 8–9, Figure 5 · p. 9

4.2 Main Results

  • Repo2Run + Claude-4 reaches F1 score 60.6, description accuracy 52.2, fix accuracy 47.3, and Pass@1 22.9, yet gaps between stages persist.The results identify difficulty translating correct feedback into robust execution actions.Source: §4.2 · p. 8, Table 3 · p. 9

4.4 Efficiency Analysis

  • More output tokens generally accompany higher error-description accuracy, but do not consistently improve Pass@1.Zero-shot Claude-4 uses three times as many tokens as zero-shot DeepSeek-V3 for only about 0.2 higher performance; Repo2Run scales more favorably with larger token budgets.Source: §4.4 · pp. 9–10, Figure 7 · p. 10

4.5 Case Study

  • Case studies show agents can identify an error type without locating its command, fail to apply a suggested fix, or introduce new faults during iterative edits.A single README may contain multiple errors that agents fail to fix completely; evaluation accepts fixes that resolve the issue even when they differ from the golden solution.Source: §4.5 · p. 10

5 Conclusion

EnConda-Bench evaluates environment-configuration trajectories alongside end-to-end executability, and the reported agents are stronger at error perception than at carrying out repairs.

5 Conclusion

  • EnConda-Bench assesses planning, diagnosis, feedback-driven repair, and final configuration actions alongside environment executability.Its task instances are generated by injecting realistic README errors and validating their effects in Docker.Source: §5 · p. 10
  • Agents show some error-perception capability but tend to classify uncertain error types as “others”.The conclusion identifies specific repair actions as a weaker area.Source: §5 · p. 11
  • The authors attribute limited repair performance to ineffective interaction and feedback mechanisms, identifying stronger environment interaction as a future direction.Agents struggle to plan feedback and interact with the real-world environment to improve repair actions.Source: §5 · p. 11

Ethics Statement

The authors describe ethical safeguards for repository use and dataset preparation, including license review, permissions, anonymization, and data cleaning.

Ethics Statement

  • Repositories were reviewed for licenses permitting the research, and permissions were obtained for data sourced from websites.The authors report permissive, copyleft, and custom licenses among the selected repositories.Source: Ethics Statement · p. 11
  • Dataset preparation included anonymization, desensitization, and data cleaning, and the authors state that samples pose no risk to public welfare.The authors characterize the proposed research directions and tasks as ethically harmless to society.Source: Ethics Statement · p. 11
  • The authors state that dataset creation follows ethical guidelines and that repository use complies with the respective licenses.They describe ethical review and license compliance as part of the dataset creation process.Source: Ethics Statement · p. 11

A Data Construction

Repository selection prioritizes reliable, buildable baselines because README error synthesis assumes the original README is correct and actionable.

A Data Construction

  • Repositories came primarily from existing human-validated, pre-filtered benchmarks, with professional annotators checking setup and excluding faulty READMEs or incomplete dependencies.Repositories were pinned and selected to preserve reliable buildable baselines for error synthesis.Source: §A.1 · p. 13
  • The authors reviewed licenses for suitability, including permissive, copyleft, and custom licenses, for the study’s intended research use.Most selected repositories used BSD, MIT, or Apache licenses; others used GPL or custom licenses judged suitable.Source: §A.1 · p. 13
  • The authors identify broader programming-language coverage as a future direction for extending evaluation across language ecosystems.The proposed expansion concerns repository coverage and environment-configuration tasks.Source: §A.1 · p. 13

A.2 Error Type Definition

The error taxonomy defines six practical categories with operational boundaries to guide environment-configuration error generation and routine evaluation.

A.2 Error Type Definition

  • The taxonomy defines six error types intended to cover common practical issues while retaining clear category boundaries.It is designed to guide subsequent error generation and routine evaluation.Source: §A.2 · pp. 13–14
  • Dependency and installation failures include missing packages or system libraries and failing installers, while command errors include invalid flags, quoting problems, or wrong working directories.These symptoms correspond to E1 and E2.Source: §A.2 · p. 14
  • File and path errors include missing or misspelled files and unavailable artifacts; environment errors include inactive environments or builds attempted before toolchain installation.These symptoms correspond to E4 and E6.Source: §A.2 · p. 14
  • Version errors include solver conflicts and ABI, GLIBC, CUDA, or library-version mismatches, while the catch-all category covers ambiguous or inconsistently named instructions.The taxonomy says curators should use the catch-all sparingly when a more specific category fits.Source: §A.2 · p. 14

A.3 Error Generation

The benchmark creates erroneous README instructions, checks that they can be executed as written, and screens generated errors for validity. It also compares annotated difficulty ratings with those of real-world environment-setup benchmarks.

A.3 Error Generation

  • Generated READMEs minimally modify an original README to inject requested errors and include error descriptions, candidate fixes, and a golden correction.The generation instructions usually limit changes to one or two sentences while preserving the rest of the README.Source: §A.3 · pp. 14–15

A.4 Automatic Validation

  • GPT-4.1-mini generates a setup script from each README and repository structure, then runs it in Docker while requiring strict adherence to the README.The script is not allowed to correct or modify the README instructions.Source: §A.4 · pp. 16–17

A.5 LLM as judge to Validate

  • GPT-4.1-mini screens each generated error for accurate classification, README support, a root-cause fix, and whether it blocks setup until fixed.Errors judged invalid are discarded.Source: §A.5 · p. 17

B Difficulty Rate

  • Professional annotators scored selected task instances on a 1–5 scale (1=very easy, 2=easy, 3=moderate, 4=difficult, 5=very difficult), considering README clarity/completeness, command executability, need for additional files/pages, and dependency burden.They sampled 100 instances each from EnvBench and their benchmark, and scored all available instances from INSTALLAMATIC Bench, ExecutionAgent Bench, and SetupBench.Source: B Difficulty Rate · p. 18

C Trajectory Training Data Genration Framework

The framework automates environment-configuration task and trajectory generation, using selected repositories and README errors to produce data for training and evaluation.

C Trajectory Training Data Genration Framework

1. Repository Selection

  • Repository selection uses thresholds of 10+ stars, 10+ issues, and 1,000+ commits.Source: §1 · p. 19

2. Error Readme Generation

  • The README error categories include dependency-installation, command-usage or syntax, file-path or missing-file, version-compatibility, and miscellaneous errors.The framework’s diagram labels command-usage or syntax errors as LLM-synthesized.Source: §2 · p. 19

D Experiment Settings

The experiment prompt asks agents to analyze README setup errors and produce a corrected, verified environment setup. The authors report using LLMs only for grammar checking and minor wording refinement during manuscript preparation.

D Experiment Settings

  • The agent prompt requests error analysis in JSON and a corrected shell script that fixes errors, configures the environment, and verifies setup.The prompt also specifies error categories, including dependency, command, path, logical-order, and version-compatibility errors.Source: D Experiment Settings · pp. 20–21
  • The setup-script requirements include examining repository structure, managing Python versions and dependencies, setting up a virtual environment, and verifying installation.The instructions also call for non-interactive commands and local-repository installation.Source: D Experiment Settings · p. 21

E The Use of Large Language Models (LLMs)

Related papers

Loading 2510.25694v1…