arXiv subjects

Profir-Petru Pârţachi

Publications and source records attributed to Profir-Petru Pârţachi.

4 recordsLinked to original sources

arXiv2026

The Construction of an Empirical Dataset of Incomplete Software Changes from Open Source Projects

During software development, a modification to a software component may propagate across the system, requiring precise identification and correct revision of all affected components. This is a complex task, and developers often (45.7%) miss related changes. To address this, several methods have been developed to extract co-change rules from files that are frequently changed together in the revision history. However, previous research evaluated the methods using artificially created incomplete changes, which may not be representative of real-world data. To solve this problem, we construct a dataset by mining incomplete changes from a collection of open-source software, using information about induced bugs and their respective fixes from an issue tracking platform. We also analyze the characteristics of incomplete changes using this constructed dataset and found that 89.4% of missed changes involved five or fewer files. Finally, we re-evaluate LCExtractor, an existing co-change rule extraction method, on our constructed dataset, and we identify the optimal sorting criterion and the impact of the number of used commits.

cs.SE↗

arXiv2026

Tangling Pull Requests: Curating a Commit Untangling Dataset from Merged PRs

Composite commits (CC), in which multiple unrelated changes are bundled into a single commit, are frequent in software development and significantly hinder code comprehension and maintenance. Although machine learning-based methods have been developed to ``untangle'' such commits into smaller, coherent change sets, these methods require large-scale training data with correct untangling labels. Preparing such datasets is costly and typically requires expert labelling. In this study, we propose a scalable and cost-effective method for dataset construction by leveraging commits extracted from open-source repositories' pull requests (PRs). We empirically validated our dataset and found that when applying our filtering rules, PRs that, when viewed as a single commit, are tangled, yet each individual commit on the feature branch is atomic (ideal PRs), increased from 9.5% to 55%. This composite commits dataset is more than 5.7 times larger than previous heuristic-based datasets. Using our new dataset, we find that the PR-based dataset differs statistically from previous datasets directly constructed using Herzig's proposed heuristics even after accounting for our proposed rules that may alter CC or STS sizes. When constructing datasets using the previous heuristics, they differ statistically along dimensions that impact the confidence voters and are likely to impact learning-based approaches. We validate the impact on the original Herzig \etal method, which used confidence voters across our dataset. To show that our approach extends to other languages, we also create a Python dataset which we empirically validate, finding comparable rates for ideal PRs (56.5%).

cs.SE↗

arXiv2026

Hallucination Inspector: A Fact-Checking Judge for API Migration

Large Language Models (LLMs) are increasingly deployed in automated software engineering for tasks such as API migration. While LLMs are able to identify migration patterns, they often make mistakes and fail to produce correct glue code to invoke the new API in place of the old one. We call this issue Scaffolding Hallucination, a failure mode where models generate incorrect calling contexts by inventing Phantom Symbols -- such as imaginary imports, constructors, and constants -- that do not exist in the API specification. In this paper, we show that standard metrics cannot be relied upon to detect these instances of hallucination. We propose Hallucination Inspector, a static analysis tool to detect Scaffolding Hallucination in LLM-generated code. Our approach includes a lightweight evaluation framework that verifies symbols extracted from the abstract syntax tree against a knowledge base derived directly from software documentation for the API. A preliminary evaluation on Android API migrations demonstrates that our approach successfully identifies hallucinations and significantly reduces false positives compared to standard metrics and probabilistic judges

cs.SE↗

arXiv2025

Bringing Structure to Naturalness: On the Naturalness of ASTs

Source code comes in different shapes and forms. Previous research has already shown code to be more predictable than natural language as well as highlighted its statistical predictability at the token level: source code can be natural. More recently, the structure of code -- control flow, syntax graphs, abstract syntax trees etc. -- has been successfully used to improve the state-of-the-art on numerous tasks: code suggestion, code summarisation, method naming etc. This body of work implicitly assumes that structured representations of code are similarly statistically predictable, i.e. that a structured view of code is also natural. We consider that this view should be made explicit and propose directly studying the Structured Naturalness Hypothesis. Beyond just naming existing research that assumes this hypothesis and formulating it, we also provide evidence in the case of trees: TreeLSTM models over ASTs for some languages, such as Ruby, are competitive with $n$-gram models while handling the syntax token issue highlighted by previous research 'for free'. For other languages, such as Java or Python, we find tree models to perform worse, suggesting that downstream task improvement is uncorrelated to the language modelling task. Further, we show how such naturalness signals can be employed for near state-of-the-art results on just-in-time defect prediction while forgoing manual feature engineering work.

cs.SE↗