arXiv subjects

Xingfang Wu

Publications and source records attributed to Xingfang Wu.

8 recordsLinked to original sources

arXiv2026

Improving the Robustness of Large Language Models for Code Tasks via Fine-tuning with Perturbed Data

Context: In the fast-paced evolution of software development, Large Language Models (LLMs) have become indispensable tools for tasks such as code generation, completion, analysis, and bug fixing. Ensuring the robustness of these models against potential vulnerabilities from handling diverse inputs is critical, as variations in input can lead to incorrect or insecure code outputs. Objective: This work aims to improve the robustness of LLMs for coding-related tasks against potential adversarial inputs. Specifically, we investigate how fine-tuning LLMs with perturbed datasets impacts their robustness against input perturbations. Method: We systematically evaluated LLM robustness by fine-tuning models using datasets perturbed at character-level, word-level, and sentence-level, comparing results against base models and models fine-tuned on unperturbed datasets. Results: Fine-tuning LLMs with perturbed datasets significantly improves model robustness (RD usually drops around 4\% - 6\%), especially for models with relatively weak robustness. However, this fine-tuning process typically results in a slight performance decrease (pass@1 usually drops around 1\% - 3\%) compared to fine-tuning with unperturbed datasets, although occasional performance improvements are observed. Conclusion \& Implications: Fine-tuning LLMs for coding tasks with perturbed data effectively enhances their robustness at the cost of a minor performance reduction, emphasizing the importance of balancing the robustness and performance of LLMs for coding applications.

cs.SE↗

arXiv2025

Plug it and Play on Logs: A Configuration-Free Statistic-Based Log Parser

Log parsing is an essential task in log analysis, and many tools have been designed to accomplish it. Existing log parsers can be categorized into statistic-based and semantic-based approaches. In comparison to semantic-based parsers, existing statistic-based parsers tend to be more efficient, require lower computational costs, and be more privacy-preserving thanks to on-premise deployment, but often fall short in their accuracy (e.g., grouping or parsing accuracy) and generalizability. Therefore, it became a common belief that statistic-based parsers cannot be as effective as semantic-based parsers since the latter could take advantage of external knowledge supported by pretrained language models. Our work, however, challenges this belief with a novel statistic-based parser, PIPLUP. PIPLUP eliminates the pre-assumption of the position of constant tokens for log grouping and relies on data-insensitive parameters to overcome the generalizability challenge, allowing "plug and play" on given log files. According to our experiments on an open-sourced large log dataset, PIPLUP shows promising accuracy and generalizability with the data-insensitive default parameter set. PIPLUP not only outperforms the state-of-the-art statistic-based log parsers, Drain and its variants, but also obtains a competitive performance compared to the best unsupervised semantic-based log parser (i.e., LUNAR). Further, PIPLUP exhibits low time consumption without GPU acceleration and external API usage; our simple, efficient, and effective approach makes it more practical in real-world adoptions, especially when costs and privacy are of major concerns.

cs.SE↗

arXiv2025

SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs

Software logs are messages recorded during the execution of a software system that provide crucial run-time information about events and activities. Although software logs have a critical role in software maintenance and operation tasks, publicly accessible log datasets remain limited, hindering advance in log analysis research and practices. The presence of sensitive information, particularly Personally Identifiable Information (PII) and quasi-identifiers, introduces serious privacy and re-identification risks, discouraging the publishing and sharing of real-world logs. In practice, log anonymization techniques primarily rely on regular expression patterns, which involve manually crafting rules to identify and replace sensitive information. However, these regex-based approaches suffer from significant limitations, such as extensive manual efforts and poor generalizability across diverse log formats and datasets. To mitigate these limitations, we introduce SDLog, a deep learning-based framework designed to identify sensitive information in software logs. Our results show that SDLog overcomes regex limitations and outperforms the best-performing regex patterns in identifying sensitive information. With only 100 fine-tuning samples from the target dataset, SDLog can correctly identify 99.5% of sensitive attributes and achieves an F1-score of 98.4%. To the best of our knowledge, this is the first deep learning alternative to regex-based methods in software log anonymization.

cs.SE↗

arXiv2025

LogLSHD: Fast Log Parsing with Locality-Sensitive Hashing and Dynamic Time Warping

Large-scale software systems generate vast volumes of system logs that are essential for monitoring, diagnosing, and performance optimization. However, the unstructured nature and ever-growing scale of these logs present significant challenges for manual analysis and automated downstream tasks such as anomaly detection. Log parsing addresses these challenges by converting raw logs into structured formats, enabling efficient log analysis. Despite its importance, existing log parsing methods suffer from limitations in efficiency and scalability, due to the large size of log data and their heterogeneous formats. To overcome these challenges, this study proposes a log parsing approach, LogLSHD, which leverages Locality-Sensitive Hashing (LSH) to group similar logs and integrates Dynamic Time Warping (DTW) to enhance the accuracy of template extraction. LogLSHD demonstrates exceptional efficiency in parsing time, significantly outperforming state-of-the-art methods. For example, compared to Drain, LogLSHD reduces the average parsing time by 73% while increasing the average parsing accuracy by 15% on the LogHub 2.0 benchmark.

cs.SE↗

arXiv2024

What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach

Log data are generated from logging statements in the source code, providing insights into the execution processes of software applications and systems. State-of-the-art log-based anomaly detection approaches typically leverage deep learning models to capture the semantic or sequential information in the log data and detect anomalous runtime behaviors. However, the impacts of these different types of information are not clear. In addition, most existing approaches ignore the timestamps in log data, which can potentially provide fine-grained sequential and temporal information. In this work, we propose a configurable Transformer-based anomaly detection model that can capture the semantic, sequential, and temporal information in the log data and allows us to configure the different types of information as the model's features. Additionally, we train and evaluate the proposed model using log sequences of different lengths, thus overcoming the constraint of existing methods that rely on fixed-length or time-windowed log sequences as inputs. With the proposed model, we conduct a series of experiments with different combinations of input features to evaluate the roles of different types of information in anomaly detection. The model can attain competitive and consistently stable performance compared to the baselines when presented with log sequences of varying lengths. The results indicate that the event occurrence information plays a key role in identifying anomalies, while the impact of the sequential and temporal information is not significant for anomaly detection on the studied public datasets. On the other hand, the findings also reveal the simplicity of the studied public datasets and highlight the importance of constructing new datasets that contain different types of anomalies to better evaluate the performance of anomaly detection models.

cs.SE↗

arXiv2023

Refining GPT-3 Embeddings with a Siamese Structure for Technical Post Duplicate Detection

One goal of technical online communities is to help developers find the right answer in one place. A single question can be asked in different ways with different wordings, leading to the existence of duplicate posts on technical forums. The question of how to discover and link duplicate posts has garnered the attention of both developer communities and researchers. For example, Stack Overflow adopts a voting-based mechanism to mark and close duplicate posts. However, addressing these constantly emerging duplicate posts in a timely manner continues to pose challenges. Therefore, various approaches have been proposed to detect duplicate posts on technical forum posts automatically. The existing methods suffer from limitations either due to their reliance on handcrafted similarity metrics which can not sufficiently capture the semantics of posts, or their lack of supervision to improve the performance. Additionally, the efficiency of these methods is hindered by their dependence on pair-wise feature generation, which can be impractical for large amount of data. In this work, we attempt to employ and refine the GPT-3 embeddings for the duplicate detection task. We assume that the GPT-3 embeddings can accurately represent the semantics of the posts. In addition, by training a Siamese-based network based on the GPT-3 embeddings, we obtain a latent embedding that accurately captures the duplicate relation in technical forum posts. Our experiment on a benchmark dataset confirms the effectiveness of our approach and demonstrates superior performance compared to baseline methods. When applied to the dataset we constructed with a recent Stack Overflow dump, our approach attains a Top-1, Top-5, and Top-30 accuracy of 23.1%, 43.9%, and 68.9%, respectively. With a manual study, we confirm our approach's potential of finding unlabelled duplicates on technical forums.

cs.SE↗

arXiv2023

Characterizing and Classifying Developer Forum Posts with their Intentions

With the rapid growth of the developer community, the amount of posts on online technical forums has been growing rapidly, which poses difficulties for users to filter useful posts and find important information. Tags provide a concise feature dimension for users to locate their interested posts and for search engines to index the most relevant posts according to the queries. However, most tags are only focused on the technical perspective (e.g., program language, platform, tool). In most cases, forum posts in online developer communities reveal the author's intentions to solve a problem, ask for advice, share information, etc. The modeling of the intentions of posts can provide an extra dimension to the current tag taxonomy. By referencing previous studies and learning from industrial perspectives, we create a refined taxonomy for the intentions of technical forum posts. Through manual labeling and analysis on a sampled post dataset extracted from online forums, we understand the relevance between the constitution of posts (code, error messages) and their intentions. Furthermore, inspired by our manual study, we design a pre-trained transformer-based model to automatically predict post intentions. The best variant of our intention prediction framework, which achieves a Micro F1-score of 0.589, Top 1-3 accuracy of 62.6% to 87.8%, and an average AUC of 0.787, outperforms the state-of-the-art baseline approach. Our characterization and automated classification of forum posts regarding their intentions may help forum maintainers or third-party tool developers improve the organization and retrieval of posts on technical forums. We have released our annotated dataset and codes in our supplementary material package.

cs.SE↗

arXiv2023

On the Effectiveness of Log Representation for Log-based Anomaly Detection

Logs are an essential source of information for people to understand the running status of a software system. Due to the evolving modern software architecture and maintenance methods, more research efforts have been devoted to automated log analysis. In particular, machine learning (ML) has been widely used in log analysis tasks. In ML-based log analysis tasks, converting textual log data into numerical feature vectors is a critical and indispensable step. However, the impact of using different log representation techniques on the performance of the downstream models is not clear, which limits researchers and practitioners' opportunities of choosing the optimal log representation techniques in their automated log analysis workflows. Therefore, this work investigates and compares the commonly adopted log representation techniques from previous log analysis research. Particularly, we select six log representation techniques and evaluate them with seven ML models and four public log datasets (i.e., HDFS, BGL, Spirit and Thunderbird) in the context of log-based anomaly detection. We also examine the impacts of the log parsing process and the different feature aggregation approaches when they are employed with log representation techniques. From the experiments, we provide some heuristic guidelines for future researchers and developers to follow when designing an automated log analysis workflow. We believe our comprehensive comparison of log representation techniques can help researchers and practitioners better understand the characteristics of different log representation techniques and provide them with guidance for selecting the most suitable ones for their ML-based log analysis workflow.

cs.SE↗