FeatMaker: Automated Feature Engineering for Search Strategy of Symbolic Execution
We present FeatMaker, a novel technique that automatically generates state features to enhance the search strategy of symbolic execution. Search strategies, designed to address the well-known state-explosion problem, prioritize which program states to explore. These strategies typically depend on a “state feature” that describes a specific property of program states, using this feature to score and rank them. Recently, search strategies employing multiple state features have shown superior performance over traditional strategies that use a single, generic feature. However, the process of designing these features remains largely manual. Moreover, manually crafting state features is both time-consuming and prone to yielding unsatisfactory results. The goal of this paper is to fully automate the process of generating state features for search strategies from scratch. The key idea is to leverage path-conditions, which are basic but vital information maintained by symbolic execution, as state features. A challenge arises when employing all path-conditions as state features, as it results in an excessive number of state features. To address this, we present a specialized algorithm that iteratively generates and refines state features based on data accumulated during symbolic execution. Experimental results on 15 open-source C programs show that FeatMaker significantly outperforms existing search strategies that rely on manually-designed features, both in terms of branch coverage and bug detection. Notably, FeatMaker achieved an average of 35.3% higher branch coverage than state-of-the-art strategies and discovered 15 unique bugs. Of these, six were detected exclusively by FeatMaker.
Wed 17 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
14:00 - 15:30 | |||
14:00 18mTalk | Test Input Prioritization for 3D Point Clouds Journal First Yinghua LI University of Luxembourg, Xueqi Dang University of Luxembourg, Lei Ma The University of Tokyo & University of Alberta, Jacques Klein University of Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg, Tegawendé F. Bissyandé University of Luxembourg | ||
14:18 18mTalk | Evaluating and Improving ChatGPT for Unit Test Generation Research Papers Zhiqiang Yuan Fudan University, Mingwei Liu Fudan University, Shiji Ding Fudan University, Kaixin Wang Fudan University, Yixuan Chen Yale University, Xin Peng Fudan University, Yiling Lou Fudan University | ||
14:36 18mTalk | Bounding Random Test Set Size with Computational Learning Theory Research Papers Neil Walkinshaw University of Sheffield, Michael Foster The University of Sheffield, José Miguel Rojas The University of Sheffield, Robert Hierons The University of Sheffield Pre-print | ||
14:54 18mTalk | COSTELLO: Contrastive Testing for Embedding-based Large Language Model as a Service Embeddings Research Papers Weipeng Jiang Xi'an Jiaotong University, Juan Zhai University of Massachusetts, Amherst, Shiqing Ma University of Massachusetts, Amherst, Xiaoyu Zhang Xi'an Jiaotong University, Chao Shen Xi'an Jiaotong University | ||
15:12 18mTalk | FeatMaker: Automated Feature Engineering for Search Strategy of Symbolic Execution Research Papers |