Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets

• URL: http://arxiv.org/abs/2310.00781v1
• Date: Sun, 1 Oct 2023 20:24:59 GMT
• Title: Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets
• Authors: Youcef Remil and Anes Bendimerad and Mathieu Chambard and Romain Mathonat and Marc Plantevit and Mehdi Kaytoue
• Abstract summary: Subgroup Discovery (SD) is a data mining method that can automatically mine incident code and extract discriminant patterns to identify the root causes of issues. We propose a novel SD approach that can handle complex target concepts with hierarchies. We apply this framework to investigate out-of-memory errors and demonstrate its usefulness in incident diagnosis.
• Score: 1.188383832081829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software applications, especially Enterprise Resource Planning (ERP) systems, are crucial to the day-to-day operations of many industries. Therefore, it is essential to maintain these systems effectively using tools that can identify, diagnose, and mitigate their incidents. One promising data-driven approach is the Subgroup Discovery (SD) technique, a data mining method that can automatically mine incident datasets and extract discriminant patterns to identify the root causes of issues. However, current SD solutions have limitations in handling complex target concepts with multiple attributes organized hierarchically. To illustrate this scenario, we examine the case of Java out-of-memory incidents among several possible applications. We have a dataset that describes these incidents, including their context and the types of Java objects occupying memory when it reaches saturation, with these types arranged hierarchically. This scenario inspires us to propose a novel Subgroup Discovery approach that can handle complex target concepts with hierarchies. To achieve this, we design a pattern syntax and a quality measure that ensure the identified subgroups are relevant, non-redundant, and resilient to noise. To achieve the desired quality measure, we use the Subjective Interestingness model that incorporates prior knowledge about the data and promotes patterns that are both informative and surprising relative to that knowledge. We apply this framework to investigate out-of-memory errors and demonstrate its usefulness in incident diagnosis. To validate the effectiveness of our approach and the quality of the identified patterns, we present an empirical study. The source code and data used in the evaluation are publicly accessible, ensuring transparency and reproducibility.

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