Pre-Conference Talk for Imam Nur Bani YUSUF and TU Haoxin

Talk #1: RecipeGen++: An Automated Trigger Action Programs Generatorh
by Imam Nur Bani YUSUF, PhD Candidate

Trigger Action Programs (TAPs) are event-driven rules that allow users to automate smart-devices and internet services. Users can write TAPs by specifying triggers and actions from a set of predefined channels and functions. Despite its simplicity, composing TAPs can still be challenging for users due to the enormous search space of available triggers and actions. Motivated by such a fact, we propose RecipeGen++, a deep-learning-based approach that can generate TAPs based on natural language descriptions. RecipeGen++ can generate TAPs in the Interactive, One-Click, or Functionality Discovery modes. In the Interactive mode, users can provide feedback to guide the prediction of a trigger or action component. In contrast, the One-Click mode allows users to generate all TAP components directly. Additionally, RecipeGen++ also enables users to discover functionalities at the channel level through the Functionality Discovery mode. We have evaluated RecipeGen++ on real-world datasets. Our results demonstrate that RecipeGen++ can outperform the baseline by 2.2%-16.2% in the gold-standard benchmark and 5%-29.2% in the noisy benchmark.

Talk #2: FastKLEE: Faster Symbolic Execution via Reducing Redundant Bound Checking of Type-Safe Pointers
by TU Haoxin, PhD Candidate

Symbolic execution (SE) has been widely adopted for automatic program analysis and software testing. Many SE engines (e.g., KLEE or Angr) need to interpret certain Intermediate Representations (IR) of code during execution, which may be slow and costly. Although a plurality of studies proposed to accelerate SE, few of them consider optimizing the internal interpretation operations. In this paper, we propose FastKLEE, a faster SE engine that aims to speed up execution via reducing redundant bound checking of type-safe pointers during IR code interpretation. Specifically, in FastKLEE, a type inference system is first leveraged to classify pointer types (i.e., safe or unsafe) for the most frequently interpreted read/write instructions. Then, a customized memory operation is designed to perform bound checking for only the unsafe pointers and omit redundant checking on safe pointers. Evaluation results demonstrate that FastKLEE is able to reduce by up to 9.1% (5.6% on average) as the state-of-the-art approach KLEE in terms of the time to explore the same number (i.e., 10k) of execution paths. FastKLEE is opensourced at https://github.com/haoxintu/FastKLEE. A video demo of FastKLEE is available at https://youtu.be/fjV_a3kt-mo.

Imam Nur Bani Yusuf is a PhD candidate at Singapore Management University under the supervision of Associate Professor Lingxiao Jiang. His research focuses on automated code generation system. The goal of his research is to develop tools and techniques to help people without prior knowledge on programming to write codes using certain specifications. More information is available at https://imamnurby.github.io.

Haoxin Tu is a Dual-degree Ph.D. candidate at SMU (Singapore Management University) and DUT (Dalian University of Technology). At SMU, he is supervised by Prof. Lingxiao Jiang and Prof. Xuhua Ding. His research focuses on developing practical techniques and tools that can help improve the reliability and security of software systems (mainly system software such as compilers and Linux kernel). More information is available at https://haoxintu.github.io/.