Breaking, Stale, or Missing? Benchmarking Coding Agents on Project-Level Test Evolution
- URL: http://arxiv.org/abs/2605.06125v1
- Date: Thu, 07 May 2026 12:31:09 GMT
- Title: Breaking, Stale, or Missing? Benchmarking Coding Agents on Project-Level Test Evolution
- Abstract summary: We present TEBench, the first project-level benchmark for test evolution.<n>We construct TEBench through a four-stage pipeline over Defects4J projects.<n>We evaluate seven configurations spanning three industrial agent frameworks.
- Score: 20.606877071567958
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As production code evolves, the test suite must co-evolve to remain effective. Existing benchmarks for test evolution operate at method-level granularity with pre-paired inputs, bypassing the task of locating affected tests from the full project and excluding the need for new tests entirely. We present TEBench, the first project-level benchmark for test evolution. Given a project repository and a code-changing commit, TEBench requires systems to autonomously identify tests requiring modification, determine where new tests are needed, and produce the corresponding test patch. We construct TEBench through a four-stage pipeline over Defects4J projects, curating 314 task instances from 10 projects with developer-written ground truth. Each instance is annotated with one or more of three evolution types: Test-Breaking (tests that fail), Test-Stale (tests that pass but no longer meaningfully validate updated behavior), and Test-Missing (new tests needed for introduced behavior). We evaluate seven configurations spanning three industrial agent frameworks (Claude Code, Codex CLI, OpenCode) and six base models, alongside a heuristic baseline. All seven configurations converge on an identification F1 of 45.7% to 49.4%, revealing a shared performance ceiling across both frameworks and base models. Test-Stale is the most challenging type, averaging F1 around 36%, since configurations rely on execution failure signals and lack proactive semantic reasoning. On the update task, configurations produce highly executable test modifications whose surface form diverges substantially from ground truth. Trajectory analysis reveals a reactive "execute-fail-fix" loop that succeeds for breaking tests but structurally cannot address stale or missing tests. TEBench is available at https://github.com/iSEngLab/TEBench with a leaderboard at https://tebench-leadership.vercel.app.
Related papers
- ExecCritic: Learn to Test, Test to Improve for Coding Agents [68.42713285082912]
Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue.<n>We introduce ExecCritic, combining a test--verify--revise scaffold with a role-specific reinforcement learning recipe for training agents within it.<n>The scaffold separates test construction from source-code repair: a Test agent independently generates repository-native tests, a fail-closed harness qualifies and freezes them, and a Repair agent revises source code from their execution feedback without changing the tests.
arXiv Detail & Related papers (2026-09-08T17:53:37Z) - How Far Are We from Detecting Flaky Tests? On the Limits of Code-Based Detection [41.369102837712795]
Flaky tests pass and fail on the same code version, weakening the signal of test results.<n>Code-based flakiness detectors report strong benchmark results, yet their use in practice remains limited.<n>We argue that flakiness is not a static property of test code, which often lacks the information needed to decide whether a test is flaky.
arXiv Detail & Related papers (2026-07-10T12:24:25Z) - TestEvo-Bench: An Executable and Live Benchmark for Test and Code Co-Evolution [3.048952775453716]
TestEvo-Bench is a benchmark of test and code co-evolution tasks mined from software repositories.<n>Each task is anchored to a real commit history and packaged with environment configuration.<n>Current snapshot contains 746 test generation and 509 test update tasks.
arXiv Detail & Related papers (2026-07-02T17:35:20Z) - An Iterative Test-and-Repair Framework for Competitive Code Generation [9.137158235106943]
Large language models (LLMs) have made remarkable progress in code generation, but competitive programming remains a challenge.<n>Recent training-based methods have improved code generation by using reinforcement learning (RL) with execution feedback.<n>The more recent framework CURE further incorporates test generation into the training process, jointly training a Coder and a Tester within a single model.
arXiv Detail & Related papers (2026-04-07T08:00:54Z) - Are Benchmark Tests Strong Enough? Mutation-Guided Diagnosis and Augmentation of Regression Suites [49.16055123488827]
Insufficiently strong test suites can admit plausible yet semantically incorrect patches, inflating reported success rates.<n>We introduce STING, a framework for targeted test augmentation that uses semantically altered program variants as diagnostic stressors.<n>STING produces 1,014 validated tests spanning 211 instances and increases patch-region line and branch coverage by 10.8% and 9.5%.
arXiv Detail & Related papers (2026-04-02T01:13:40Z) - WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing [57.7131457251794]
We introduce WebTestBench, a benchmark for evaluating end-to-end automated web testing.<n>We decompose the testing process into two cascaded sub-tasks, checklist generation and defect detection, and propose WebTester.<n>Our findings expose a substantial gap between current computer-use agent capabilities and industrial-grade deployment demands.
arXiv Detail & Related papers (2026-03-26T09:27:29Z) - Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesis [57.40527331817245]
Test oracle generation in non-regression testing is a longstanding challenge in software engineering.<n>We introduce Nexus, a novel multi-agent framework to address this challenge.
arXiv Detail & Related papers (2025-10-30T12:20:25Z) - ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases [58.411135609139855]
"Shortcuts" to complete tasks pose significant risks for reliable assessment and deployment of large language models.<n>We introduce ImpossibleBench, a benchmark framework that measures LLM agents' propensity to exploit test cases.<n>As a practical framework, ImpossibleBench is not just an evaluation but a versatile tool.
arXiv Detail & Related papers (2025-10-23T06:58:32Z) - Intention-Driven Generation of Project-Specific Test Cases [45.2380093475221]
We propose IntentionTest, which generates project-specific tests given the description of validation intention.<n>We extensively evaluate IntentionTest against state-of-the-art baselines (DA, ChatTester, and EvoSuite) on 4,146 test cases from 13 open-source projects.
arXiv Detail & Related papers (2025-07-28T08:35:04Z) - ProjectTest: A Project-level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms [48.43237545197775]
Unit test generation has become a promising and important use case of LLMs.<n>ProjectTest is a project-level benchmark for unit test generation covering Python, Java, and JavaScript.
arXiv Detail & Related papers (2025-02-10T15:24:30Z) - AugmenTest: Enhancing Tests with LLM-Driven Oracles [2.159639193866661]
AugmenTest is an approach leveraging Large Language Models to infer correct test oracles based on available documentation of the software under test.<n>AugmenTest includes four variants: Simple Prompt, Extended Prompt, RAG with a generic prompt (without the context of class or method under test), and RAG with Simple Prompt, each offering different levels of contextual information to the LLMs.<n>Results show that in the most conservative scenario, AugmenTest's Extended Prompt consistently outperformed the Simple Prompt, achieving a success rate of 30% for generating correct assertions.
arXiv Detail & Related papers (2025-01-29T07:45:41Z) - TestGenEval: A Real World Unit Test Generation and Test Completion Benchmark [24.14654309612826]
TestGenEval comprises 68,647 tests from 1,210 code and test file pairs across 11 well-maintained Python repositories.<n>It covers initial tests authoring, test suite completion, and code coverage improvements.<n>We evaluate several popular models, with sizes ranging from 7B to 405B parameters.
arXiv Detail & Related papers (2024-10-01T14:47:05Z) - Observation-based unit test generation at Meta [52.4716552057909]
TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution.
TestGen has landed 518 tests into production, which have been executed 9,617,349 times in continuous integration, finding 5,702 faults.
Our evaluation reveals that, when carving its observations from 4,361 reliable end-to-end tests, TestGen was able to generate tests for at least 86% of the classes covered by end-to-end tests.
arXiv Detail & Related papers (2024-02-09T00:34:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.