Crowdsourced testing has emerged as a prominent paradigm in software testing by leveraging the diversity of crowdworkers. In this paradigm, crowd-workers are required to submit a test report for each identified bug, which typically contains a textual description and a bug screenshot. However, due to varying worker expertise, many reports exhibit inconsistencies between the textual description and the bug screenshot, which hinder the report review process. Existing methods address this issue by automatically detecting report consistency, typically through matching the UI widgets referenced in the textual description with those visible in the bug screenshot. However, such methods focus only on surface-level element correspondence and fail to capture the abstract bug semantics, such as the functional meaning and bug-triggering context. Consequently, they lack the ability to detect more subtle but realistic inconsistencies. To bridge this gap, we propose INCONHUNTER, a novel method for crowdsourced test report consistency detection that explicitly simulates human cognitive pattern. In this pattern, humans typically adopt two complementary reasoning strategies. If the textual description allows them to form an expectation about the visual bug features, they assess consistency by verifying if the expected features appear in the bug screenshot. Otherwise, they shift to reasoning about if the bug-triggering context described in the report aligns with the app state shown in the bug screenshot. INCONHUNTERinstantiates this cognitive pattern through two LLM-powered modules, each dedicated to one reasoning strategy. We evaluate INCONHUNTERthrough experiments on our dataset with 2,310 labeled crowdsourced test reports, and results show that INCONHUNTERoutperforms baselines by 14.00%–19.28%, demonstrating superior effectiveness, monetary-based cost efficiency, and alignment with human cognitive pattern.

In the field of software development, the application of code quality check tools has become a key factor in improving product quality and development efficiency. While many existing tools are effective at detecting common problems in code, there are still some limitations. Firstly, these tools rely on predefined rules that may not fully encompass real-world coding challenges. Secondly, a lack of consideration of dependencies leads to failure to report violations occurring across files or modules. Third, the metrics used by these tools primarily focus on object-oriented programming, limiting their ability to assess software quality from the perspective of nationalized standards. To address these issues, this work proposes a dependency-enhanced method namely ERD-CQC for code quality detection and measurement. ERD-CQC provides 88 detection rules and 45 metrics, supplementing checking rules in categories such as Circuit Breaking, Serializable, and Security. ERD-CQC constructs an infused graph by integrating abstract syntax trees (ASTs), entities, and dependencies for violation detection. Based on the detection results, ERD-CQC provides a code quality measurement system with 4 nationalized standard dimensions for the purpose of measuring code quality from multiple perspectives. To validate the effectiveness of ERD-CQC, we manually examined 647 compliant and 528 non-compliant code snippets. ERD-CQC achieves the recall and F1 score exceeding 98%. We also collected open-source projects and closed-source projects in the real world, containing a total of 4,319 non-compliant code snippets. On this real-world benchmark, the average F1 score of ERD-CQC is 11.44% higher than the advanced tool SonarQube. Finally, we visualized the quality measurement results based on metrics and found that open-source and closed-source projects have certain patterns in metric performance. Our work will benefit developers in checking, evaluating, and monitoring their software quality comprehensively.