ISSTA 2022
Mon 18 - Fri 22 July 2022 Online

Developers insert logging statements into source code to monitor system execution, which forms the basis for software debugging and maintenance. For distinguishing diverse runtime information, each software log is assigned with a separate verbosity level (e.g., trace and error). However, choosing an appropriate verbosity level is a challenging and error-prone task due to the lack of specifications for log level usages. Prior solutions aim to suggest log levels based on the code block in which a logging statement resides (i.e., intra-block features). However, such suggestions do not consider information from surrounding blocks (i.e., inter-block features), which also plays an important role in revealing logging characteristics.

To address this issue, we combine multiple levels of code block information (i.e., intra-block and inter-block features) into a joint graph structure called Flow of Abstract Syntax Tree (FAST). To explicitly exploit multi-level block features, we design a new neural architecture, Hierarchical Block Graph Network (HBGN), on the FAST. In particular, it leverages graph neural networks to encode both the intra-block and inter-block features into code block representations and guide log level suggestions. We implement a prototype system, TeLL, and evaluate its effectiveness on nine large-scale software systems. Experimental results showcase TeLL’s advantage in predicting log levels over the state-of-the-art approaches.

Wed 20 Jul

Displayed time zone: Seoul change

08:40 - 09:40
Session 2-3: Oracles, Models, and Measurement BTechnical Papers at ISSTA 1
08:40
20m
Talk
TELL: Log Level Suggestions via Modeling Multi-level Code Block Information
Technical Papers
Jiahao Liu National University of Singapore, Jun Zeng National University of Singapore, Xiang Wang University of Science and Technology of China, Kaihang Ji National University of Singapore, Zhenkai Liang National University of Singapore
DOI
09:00
20m
Talk
Hunting Bugs with Accelerated Optimal Graph Vertex Matching
Technical Papers
Xiaohui Zhang Renmin University of China, Yuanjun Gong Renmin University of China, Bin Liang Renmin University of China, China, Jianjun Huang Renmin University of China, China, Wei You Renmin University of China, Wenchang Shi Renmin University of China, China, Jian Zhang Institute of Software at Chinese Academy of Sciences, China
DOI
09:20
20m
Talk
Using Pre-trained Language Models to Resolve Textual and Semantic Merge Conflicts (Experience Paper)
Technical Papers
Jialu Zhang Yale University, Todd Mytkowicz Microsoft Research, Mike Kaufman Microsoft Corporation, Ruzica Piskac Yale University, Shuvendu K. Lahiri Microsoft Research
DOI

Fri 22 Jul

Displayed time zone: Seoul change

16:40 - 17:40
Session 3-11: Oracles, Models, and Measurement CTechnical Papers at ISSTA 1
16:40
20m
Talk
An Extensive Study on Pre-trained Models for Program Understanding and Generation
Technical Papers
Zhengran Zeng Southern University of Science and Technology, Hanzhuo Tan Southern University of Science and Technology, The Hong Kong Polytechnic University, Haotian Zhang , Jing Li The Hong Kong Polytech University, Yuqun Zhang Southern University of Science and Technology, Lingming Zhang University of Illinois at Urbana-Champaign
DOI
17:00
20m
Talk
Metamorphic Relations via Relaxations: An Approach to Obtain Oracles for Action-Policy Testing
Technical Papers
Hasan Ferit Eniser MPI-SWS, Timo P. Gros Saarland University, Germany, Valentin Wüstholz ConsenSys, Jörg Hoffmann Saarland University and DFKI, Germany, Maria Christakis MPI-SWS
DOI Pre-print
17:20
20m
Talk
TELL: Log Level Suggestions via Modeling Multi-level Code Block Information
Technical Papers
Jiahao Liu National University of Singapore, Jun Zeng National University of Singapore, Xiang Wang University of Science and Technology of China, Kaihang Ji National University of Singapore, Zhenkai Liang National University of Singapore
DOI