SaaSBench introduces a heterogeneous benchmark for enterprise SaaS engineering and shows that state-of-the-art coding agents fail over 95% of the time before reaching deep business logic due to setup and integration problems.
Projdevbench: Benchmarking AI coding agents on end-to-end project development.CoRR, abs/2602.01655
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.SE 3years
2026 3roles
dataset 1polarities
background 1representative citing papers
ABTest mines 400 failure reports into 47 patterns and 128 actions to generate 647 tests that flag 642 new anomalies across three AI coding agents at 40.8% precision.
AI IDEs with structured guidance can produce functional large-scale code but frequently introduce design flaws such as duplication, complexity, and principle violations that risk long-term maintainability.
citing papers explorer
-
SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering
SaaSBench introduces a heterogeneous benchmark for enterprise SaaS engineering and shows that state-of-the-art coding agents fail over 95% of the time before reaching deep business logic due to setup and integration problems.
-
ABTest: Behavior-Driven Testing for AI Coding Agents
ABTest mines 400 failure reports into 47 patterns and 128 actions to generate 647 tests that flag 642 new anomalies across three AI coding agents at 40.8% precision.
-
Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects
AI IDEs with structured guidance can produce functional large-scale code but frequently introduce design flaws such as duplication, complexity, and principle violations that risk long-term maintainability.