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新论文 · v1arXiv:2611.14940

TSBugs Explorer: Exploring and Benchmarking TypeScript Bugs

TianYi Tang, Saba Alimadadi

原文摘要

Curated datasets of real-world software bugs support empirical research in software testing, debugging, and automated program repair. However, exploring heterogeneous bug collections, comparing fault characteristics across projects, and constructing research-specific subsets often require manual inspection and custom scripts. We present TSBugs Explorer, an interactive visual analytics platform built upon our previously-published dataset of 633 bugs from 16 open-source TypeScript repositories, classified into 11 fault categories. The platform integrates bug records, repository characteristics, dependency information, and taxonomy guidance through coordinated visualizations, interactive filtering, and detailed bug inspection. It supports alternative aggregation and normalization of fault distributions, pairwise repository comparisons, and analysis of differences in observed fault profiles. An integrated benchmark builder allows researchers to select bug subsets using multiple criteria, inspect their composition, and export records in CSV or JSON format. We demonstrate the platform through three research workflows: fault characterization, cross-repository comparison, and task-specific benchmark construction. Functional validation by two authors confirmed selection correctness, visualization consistency, export integrity, and reproducibility across the tested workflows. By making the existing corpus easier to explore, compare, and reuse, TSBugs Explorer facilitates systematic, research-specific selection and export of TypeScript bug records.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14937

Caprese: Change Impact Recommendation Using History and Runtime Analysis

Sadjad Tavakoli, Saba Alimadadi

原文摘要

Change impact recommendation identifies program entities potentially affected by source code changes. Historical co-changes and runtime dependencies provide complementary evidence, particularly in JavaScript, where dynamic and asynchronous behaviour complicates dependency analysis. We present Caprese, an open-source framework for function-level change impact recommendation in Node.js. Given a change set, it produces a ranked impact set of potentially affected functions. It supports repository-based recommendation through confidence-guided mining of recurring, change-set-relevant co-change patterns of varying lengths; dynamic recommendation through execution-derived dependencies involving calls, callbacks, events, timers, and shared state; and hybrid recommendation integrating both analyses. Its modular implementation supports reusable analyses. Evaluation on ten open-source applications and 100 selected change sets shows that, on average across projects, 77.4% of the relevant functions among the top 60 hybrid recommendations are identified exclusively by one analysis, demonstrating substantial complementarity. Dynamic recommendation achieves the highest mean precision among the top five recommendations (0.65), while repository-based recommendation performs best when the ranking is extended to 60 recommendations (0.42). Hybrid recommendation outperforms TARMAQ on average but does not consistently outperform the individual modes. Caprese is publicly available with source code, documentation, and evaluation artifacts.

cs.SE

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arXiv ↗PDF ↗