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Researcher profile

Xiaoli Lian

Xiaoli Lian contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Software EngineeringArtificial Intelligenceastro-ph.GAastro-ph.COComputation and Language

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Published work

5 published item(s)

preprint2026arXiv

MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair

Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can operate reliably at repository scale. Although Large Language Model (LLM)-based agents have recently shown promise for automated vulnerability repair (AVR), most existing systems still treat repair as a single generation step over the currently visible code context. As a result, they lack a persistent mechanism for reusing prior fixes or learning from failed validation attempts, which limits their effectiveness on complex, multi-file repair tasks. We present MemRepair, a memory-augmented agentic framework that formulates vulnerability repair as an iterative, experience-driven process. MemRepair combines three complementary memory layers, i.e., History-Fix, Security-Pattern, and Refinement-Trajectory memories, with a dynamic feedback-driven refinement loop. This design allows the agent to retrieve repository-specific repair conventions, apply reusable security defenses, and exploit prior "failure-to-success" trajectories to revise semantically invalid patches based on runtime evidence. We evaluate MemRepair on three representative repository-level vulnerability repair benchmarks: SEC-Bench, PatchEval (Python, Go, JavaScript), and the C++ subset of Multi-SWE-bench. MemRepair achieves state-of-the-art resolution rates of 58.0%, 58.2%, and 30.58%, respectively, outperforming strong general-purpose agents such as OpenHands and SWE-agent, as well as the specialized AVR tool InfCode-C++, while maintaining competitive repair cost. These results show that persistent, hierarchical repair memory can substantially improve the reliability of agentic vulnerability repair across diverse languages and repository settings.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Preliminary Study on the Potential Usefulness of Open Domain Model for Missing Software Requirements Recommendation

Completeness is one of the most important attributes of software requirement specifications. Unfortunately, incompleteness is meanwhile one of the most difficult problems to detect. Some approaches have been proposed to detect missing requirements based on the requirement-oriented domain model. However, this kind of models are lacking for lots of domains. Fortunately, the domain models constructed for different purposes can usually be found online. This raises a question: whether or not these domain models are helpful in finding the missing functional information in requirement specification? To explore this question, we design and conduct a preliminary study by computing the overlapping rate between the entities in domain models and the concepts of natural language software requirements and then digging into four regularities of the occurrence of these entities(concepts) based on two example domains. The usefulness of these regularities, especially the one based on our proposed metric AHME (with F2 gains of 146% and 223% on the two domains than without any regularity), has been shown in experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Automatically detecting the conflicts between software requirements based on finer semantic analysis

Context: Conflicts between software requirements bring uncertainties to product development. Some great approaches have been proposed to identify these conflicts. However, they usually require the software requirements represented with specific templates and/or depend on other external source which is often uneasy to build for lots of projects in practice. Objective: We aim to propose an approach Finer Semantic Analysis-based Requirements Conflict Detector (FSARC) to automatically detecting the conflicts between the given natural language functional requirements by analyzing their finer semantic compositions. Method: We build a harmonized semantic meta-model of functional requirements with the form of eight-tuple. Then we propose algorithms to automatically analyze the linguistic features of requirements and to annotate the semantic elements for their semantic model construction. And we define seven types of conflicts as long as their heuristic detecting rules on the ground of their text pattern and semantical dependency. Finally, we design and implement the algorithm for conflicts detection. Results: The experiment with four requirement datasets illustrates that the recall of FSARC is nearly 100% and the average precision is 83.88% on conflicts detection. Conclusion: We provide a useful tool for detecting the conflicts between natural language functional requirements to improve the quality of the final requirements set. Besides, our approach is capable of transforming the natural language functional requirements into eight semantic tuples, which is useful not only the detection of the conflicts between requirements but also some other tasks such as constructing the association between requirements and so on.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Monte Carlo Implementation of Galactic Free-Free Emission for the EoR Foreground Models

The overwhelming foreground causes severe contamination on the detection of 21-cm signal during the Epoch of Reionization (EoR). Among various foreground components, the Galactic free-free emission is less studied, so that its impact on the EoR observation remains unclear. To better constrain this emission, we perform the Monte Carlo simulation of H$α$ emission, which comprises direct and scattered H$α$ radiation from HII regions and warm ionized medium (WIM). The positions and radii of HII regions are quoted from the WISE HII catalog, and the WIM is described by an axisymmetric model. The scattering is off dust and free electrons that are realized by applying an exponential fitting to the HI4PI HI map and an exponential disk model, respectively. The simulated H$α$ intensity, the Simfast21 software, and the latest SKA1-Low layout configuration are employed to simulate the SKA &#34;observed&#34; images of Galactic free-free emission and the EoR signal. By analyzing the one-dimensional power spectra, we find that the Galactic free-free emission can be about $10^{5.4}$-$10^{2.1}$, $10^{5.0}$-$10^{1.7}$, and $10^{4.3}$-$10^{1.1}$ times more luminous than the EoR signal on scales of $0.1~{\rm Mpc^{-1}} < k < 2~{\rm Mpc^{-1}}$ in the 116-124, 146-154, and 186-194 MHz frequency bands, respectively. We further calculate the two-dimensional power spectra inside the EoR window and show that the power leaked by Galactic free-free emission can still be significant, as the power ratios can reach about $110\%$-$8000\%$, $30\%$-$2400\%$, and $10\%$-$250\%$ on scales of $0.5~{\rm Mpc^{-1}} \lesssim k \lesssim 1~{\rm Mpc^{-1}}$ in three frequency bands. Therefore, we indicate that the Galactic free-free emission should be carefully treated in future EoR detections.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Contribution of Galactic free-free emission to the foreground for EoR signal in SKA experiments

The overwhelming foreground contamination hinders the accurate detection of the 21-cm signal of neutral hydrogen during the Epoch of Reionization (EoR). Among various foreground components, the Galactic free-free emission is less studied, so that its impact on the EoR observations remains unclear. In this work, we employ the observed $\rm Hα$ intensity map with the correction of dust absorption and scattering, the Simfast21 software, and the latest SKA1-Low layout configuration to simulate the SKA &#34;observed&#34; images of Galactic free-free emission and the EoR signal. By calculating the one-dimensional power spectra from the simulated image cubes, we find that the Galactic free-free emission is about $10^{3.5}$-$10^{2.0}$, $10^{3.0}$-$10^{1.3}$, and $10^{2.5}$-$10^{1.0}$ times more luminous than the EoR signal on scales of $0.1~\rm Mpc^{-1} < k < 2~\rm Mpc^{-1}$ in the $116$-$124$, $146$-$154$, and $186$-$194$ ${\rm MHz}$ frequency bands. We further analyse the two-dimensional power spectra inside the properly defined EoR window and find that the leaked Galactic free-free emission can still cause non-negligible contamination, as the ratios of its power (amplitude squared) to the EoR signal power can reach about $200\%$, $60\%$, and $15\%$ on scales of $1.2~\rm Mpc^{-1}$ in three frequency bands, respectively. Therefore, we conclude that the Galactic free-free emission, as a severe contaminating foreground component, needs to be carefully treated in the forthcoming deep EoR observations.

0 citations0 reviewsNo code linkedOpen access