arXiv subjects

Qiaolin Qin

Publications and source records attributed to Qiaolin Qin.

8 recordsLinked to original sources

arXiv2025

A Story About Cohesion and Separation: Label-Free Metric for Log Parser Evaluation

Log parsing converts log messages into structured event templates, allowing for automated log analysis and reducing manual inspection effort. To select the most compatible parser for a specific system, multiple evaluation metrics are commonly used for performance comparisons. However, existing evaluation metrics heavily rely on labeled log data, which limits prior studies to a fixed set of datasets and hinders parser evaluations and selections in the industry. Further, we discovered that different versions of ground-truth used in existing studies can lead to inconsistent performance conclusions. Motivated by these challenges, we propose a novel label-free template-level metric, PMSS (parser medoid silhouette score), to evaluate log parser performance. PMSS evaluates both parser grouping and template quality with medoid silhouette analysis and Levenshtein distance within a near-linear time complexity in general. To understand its relationship with label-based template-level metrics, FGA and FTA, we compared their evaluation outcomes for six log parsers on the standard corrected Loghub 2.0 dataset. Our results indicate that log parsers achieving the highest PMSS or FGA exhibit comparable performance, differing by only 2.1% on average in terms of the FGA score; the difference is 9.8% for FTA. PMSS is also significantly (p<1e-8) and positively correlated to both FGA and FTA: the Spearman's rho correlation coefficient of PMSS-FGA and PMSS-FTA are respectively 0.648 and 0.587, close to the coefficient between FGA and FTA (0.670). We further extended our discussion on how to interpret the conclusions from different metrics, identifying challenges in using PMSS, and provided guidelines on conducting parser selections with our metric. PMSS provides a valuable evaluation alternative when ground-truths are inconsistent or labels are unavailable.

cs.SE↗

arXiv2025

On the Role and Impact of GenAI Tools in Software Engineering Education

Context. The rise of generative AI (GenAI) tools like ChatGPT and GitHub Copilot has transformed how software is learned and written. In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning. Objective. This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences. Method. We conducted a survey with 130 undergraduate students from two universities. The survey combined structured Likert-scale items and open-ended questions to investigate five dimensions: usage context, perceived benefits, challenges, ethical and instructional perceptions. Results. Students most often use GenAI for incremental learning and advanced implementation, reporting benefits such as brainstorming support and confidence-building. At the same time, they face challenges including unclear rationales and difficulty adapting outputs. Students highlight ethical concerns around fairness and misconduct, and call for clearer instructional guidance. Conclusion. GenAI is reshaping SE education in nuanced ways. Our findings underscore the need for scaffolding, ethical policies, and adaptive instructional strategies to ensure that GenAI supports equitable and effective learning.

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↗

arXiv2024

Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection

With the advent of data-centric and machine learning (ML) systems, data quality is playing an increasingly critical role in ensuring the overall quality of software systems. Data preparation, an essential step towards high data quality, is known to be a highly effort-intensive process. Although prior studies have dealt with one of the most impacting issues, data pattern violations, these studies usually require data-specific configurations (i.e., parameterized) or use carefully curated data as learning examples (i.e., supervised), relying on domain knowledge and deep understanding of the data, or demanding significant manual effort. In this paper, we introduce RIOLU: Regex Inferencer auto-parameterized Learning with Uncleaned data. RIOLU is fully automated, automatically parameterized, and does not need labeled samples. RIOLU can generate precise patterns from datasets in various domains, with a high F1 score of 97.2%, exceeding the state-of-the-art baseline. In addition, according to our experiment on five datasets with anomalies, RIOLU can automatically estimate a data column's error rate, draw normal patterns, and predict anomalies from unlabeled data with higher performance (up to 800.4% improvement in terms of F1) than the state-of-the-art baseline, even outperforming ChatGPT in terms of both accuracy (12.3% higher F1) and efficiency (10% less inference time). A variant of RIOLU, with user guidance, can further boost its precision, with up to 37.4% improvement in terms of F1. Our evaluation in an industrial setting further demonstrates the practical benefits of RIOLU.

cs.SE↗

arXiv2024

Preprocessing is All You Need: Boosting the Performance of Log Parsers With a General Preprocessing Framework

Log parsing has been a long-studied area in software engineering due to its importance in identifying dynamic variables and constructing log templates. Prior work has proposed many statistic-based log parsers (e.g., Drain), which are highly efficient; they, unfortunately, met the bottleneck of parsing performance in comparison to semantic-based log parsers, which require labeling and more computational resources. Meanwhile, we noticed that previous studies mainly focused on parsing and often treated preprocessing as an ad hoc step (e.g., masking numbers). However, we argue that both preprocessing and parsing are essential for log parsers to identify dynamic variables: the lack of understanding of preprocessing may hinder the optimal use of parsers and future research. Therefore, our work studied existing log preprocessing approaches based on Loghub, a popular log parsing benchmark. We developed a general preprocessing framework with our findings and evaluated its impact on existing parsers. Our experiments show that the preprocessing framework significantly boosts the performance of four state-of-the-art statistic-based parsers. Drain, the best statistic-based parser, obtained improvements across all four parsing metrics (e.g., F1 score of template accuracy, FTA, increased by 108.9%). Compared to semantic-based parsers, it achieved a 28.3% improvement in grouping accuracy (GA), 38.1% in FGA, and an 18.6% increase in FTA. Our work pioneers log preprocessing and provides a generalizable framework to enhance log parsing.

cs.SE↗

arXiv2024

Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study

Code reviews are an integral part of software development and have been recognized as a crucial practice for minimizing bugs and favouring higher code quality. They serve as an important checkpoint before committing code and play an essential role in knowledge transfer between developers. However, code reviews can be time-consuming and can stale the development of large software projects. In a recent study, Guo et al. assessed how ChatGPT3.5 can help the code review process. They evaluated the effectiveness of ChatGPT in automating the code refinement tasks, where developers recommend small changes in the submitted code. While Guo et al. 's study showed promising results, proprietary models like ChatGPT pose risks to data privacy and incur extra costs for software projects. In this study, we explore alternatives to ChatGPT in code refinement tasks by including two open-source, smaller-scale large language models: CodeLlama and Llama 2 (7B parameters). Our results show that, if properly tuned, the Llama models, particularly CodeLlama, can achieve reasonable performance, often comparable to ChatGPT in automated code refinement. However, not all code refinement tasks are equally successful: tasks that require changing existing code (e.g., refactoring) are more manageable for models to automate than tasks that demand new code. Our study highlights the potential of open-source models for code refinement, offering cost-effective, privacy-conscious solutions for real-world software development.

cs.SE↗

arXiv2024

Understanding Web Application Workloads and Their Applications: Systematic Literature Review and Characterization

Web applications, accessible via web browsers over the Internet, facilitate complex functionalities without local software installation. In the context of web applications, a workload refers to the number of user requests sent by users or applications to the underlying system. Existing studies have leveraged web application workloads to achieve various objectives, such as workload prediction and auto-scaling. However, these studies are conducted in an ad hoc manner, lacking a systematic understanding of the characteristics of web application workloads. In this study, we first conduct a systematic literature review to identify and analyze existing studies leveraging web application workloads. Our analysis sheds light on their workload utilization, analysis techniques, and high-level objectives. We further systematically analyze the characteristics of the web application workloads identified in the literature review. Our analysis centers on characterizing these workloads at two distinct temporal granularities: daily and weekly. We successfully identify and categorize three daily and three weekly patterns within the workloads. By providing a statistical characterization of these workload patterns, our study highlights the uniqueness of each pattern, paving the way for the development of realistic workload generation and resource provisioning techniques that can benefit a range of applications and research areas.

cs.SE↗

arXiv2024

Wrangling Data Issues to be Wrangled: Literature Review, Taxonomy, and Industry Case Study

Data quality is vital for user experience in products reliant on data. As solutions for data quality problems, researchers have developed various taxonomies for different types of issues. However, although some of the existing taxonomies are near-comprehensive, the over-complexity has limited their actionability in data issue solution development. Hence, recent researchers issued new sets of data issue categories that are more concise for better usability. Although more concise, modern data issue labeling's over-catering to the solution systems may sometimes cause the taxonomy to be not mutually exclusive. Consequently, different categories sometimes overlap in determining the issue types, or the same categories share different definitions across research. This hinders solution development and confounds issue detection. Therefore, based on observations from a literature review and feedback from our industry partner, we propose a comprehensive taxonomy of data quality issues from two distinct dimensions: the attribute dimension represents the intrinsic characteristics and the outcome dimension that indicates the manifestation of the issues. With the categories redefined, we labeled the reported data issues in our industry partner's data warehouse. The labeled issues provide us with a general idea of the distributions of each type of problem and which types of issues require the most effort and care to deal with. Our work aims to address a widely generalizable taxonomy rule in modern data quality issue engineering and helps practitioners and researchers understand their data issues and estimate the efforts required for issue fixing.

cs.DB↗