Xiaodong Liu

Xiaopeng Li, Shasha Li, Xi Wang et al.

Large Language Models (LLMs) power numerous AI applications, yet updating their knowledge remains costly. Model editing provides a lightweight alternative through targeted parameter modifications, with meta-learning-based model editing (MLME) demonstrating strong effectiveness and efficiency. However, we find that MLME struggles in low-data regimes and incurs high training costs due to the use of KL divergence. To address these issues, we propose $\textbf{E}$fficient $\textbf{M}$ulti-$\textbf{S}$tep $\textbf{Edit (EMSEdit)}$, which leverages multi-step backpropagation (MSBP) to effectively capture gradient-activation mapping patterns within editing samples, performs multi-step edits per sample to enhance editing performance under limited data, and introduces norm-based regularization to preserve unedited knowledge while improving training efficiency. Experiments on two datasets and three LLMs show that EMSEdit consistently outperforms state-of-the-art methods in both sequential and batch editing. Moreover, MSBP can be seamlessly integrated into existing approaches to yield additional performance gains. Further experiments on a multi-hop reasoning editing task demonstrate EMSEdit's robustness in handling complex edits, while ablation studies validate the contribution of each design component. Our code is available at https://github.com/xpq-tech/emsedit.

Shulin Zhou, Xiaodong Liu, Shanshan Li et al.

Misconfigurations have become the dominant causes of software failures in recent years, drawing tremendous attention for their increasing prevalence and severity. Configuration constraints can preemptively avoid misconfiguration by defining the conditions that configuration options should satisfy. Documentation is the main source of configuration constraints, but it might be incomplete or inconsistent with the source code. In this regard, prior researches have focused on obtaining configuration constraints from software source code through static analysis. However, the difficulty in pointer analysis and context comprehension prevents them from collecting accurate and comprehensive constraints. In this paper, we observed that software logs often contain configuration constraints. We conducted an empirical study and summarized patterns of configuration-related log messages. Guided by the study, we designed and implemented ConfInLog, a static tool to infer configuration constraints from log messages. ConfInLog first selects configuration-related log messages from source code by using the summarized patterns, then infers constraints from log messages based on the summarized natural language patterns. To evaluate the effectiveness of ConfInLog, we applied our tool on seven popular open-source software systems. ConfInLog successfully inferred 22 to 163 constraints, in which 59.5% to 61.6% could not be inferred by the state-of-the-art work. Finally, we submitted 67 documentation patches regarding the constraints inferred by ConfInLog. The constraints in 29 patches have been confirmed by the developers, among which 10 patches have been accepted.

Zhouyang Jia, Shanshan Li, Tingting Yu et al.

Applications depend on libraries to avoid reinventing the wheel. Libraries may have incompatible changes during evolving. As a result, applications will suffer from compatibility failures. There has been much research on addressing detecting incompatible changes in libraries, or helping applications co-evolve with the libraries. The existing solution helps the latest application version work well against the latest library version as an afterthought. However, end users have already been suffering from the failures and have to wait for new versions. In this paper, we propose DepOwl, a practical tool helping users prevent compatibility failures. The key idea is to avoid using incompatible versions from the very beginning. We evaluated DepOwl on 38 known compatibility failures from StackOverflow, and DepOwl can prevent 32 of them. We also evaluated DepOwl using the software repository shipped with Ubuntu-19.10. DepOwl detected 77 unknown dependency bugs, which may lead to compatibility failures.