ICPC 2026
Sun 12 - Mon 13 April 2026 Rio de Janeiro, Brazil
co-located with ICSE 2026

Symbolic execution is a widely used technique for test generation, offering systematic exploration of program paths through constraint solving. However, it is fundamentally constrained by the capability to model the target code, including library functions, in terms of symbolic constraints and by the capability of underlying constraint solvers. As a result, many paths involving complex features remain unanalyzed or insufficiently modeled. Recent advances in large language models (LLMs) have shown promise in generating diverse and valid test inputs. Yet, LLMs lack mechanisms for systematically enumerating program paths and often fail to cover subtle corner cases. We observe that directly prompting an LLM with the full program leads to missed coverage of interesting paths. In this paper, we present PALM, a test generation system that combines symbolic path enumeration with LLM-assisted test generation. PALM statically enumerates possible paths through AST-level analysis and transforms each into an executable variant with embedded assertions that specify the target path. This avoids the need to translate path constraints into SMT formulas, by instead constructing program variants that the LLM can interpret. Importantly, PALM provides an interactive frontend that visualizes path coverage alongside generated tests, assembling tests based on the specific paths they exercise. A user study with 12 participants demonstrates that PALM’s frontend helps users better understand path coverage and identify which paths are actually exercised by PALM-generated tests through verification and visualization of their path profiles.

Mon 13 Apr

Displayed time zone: Brasilia, Distrito Federal, Brazil change

14:00 - 15:30
Session 6 - LLM-based Code Generation and UnderstandingResearch Track / ICPC Program at Europa II
Chair(s): Banani Roy University of Saskatchewan
14:00
10m
Talk
Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation
Research Track
Alessandro Giagnorio Software Institute @ Università della Svizzera italiana, Antonio Mastropaolo William and Mary, USA, Saima Afrin William and Mary, USA, Massimiliano Di Penta University of Sannio, Italy, Gabriele Bavota Software Institute @ Università della Svizzera Italiana
Pre-print
14:10
10m
Talk
Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization
Research Track
Alessandro Midolo University of Catania, Alessandro Giagnorio Software Institute @ Università della Svizzera italiana, Fiorella Zampetti University of Sannio, Italy, Rosalia Tufano Università della Svizzera Italiana, Gabriele Bavota Software Institute @ Università della Svizzera Italiana, Massimiliano Di Penta University of Sannio, Italy
Pre-print
14:20
10m
Talk
From Generation to Reasoning: Chain-of-Thought Guided Merge Conflict Resolution
Research Track
Chunyou Peng Southwest University, Zhengnan Zhang Southwest University, China, Shmuel Tyszberowicz The Academic College of Tel-Aviv Yaffo, Zhiming Liu Southwest University, Bo Liu Southwest University
14:30
10m
Talk
Algorithm-Based Pipeline for Reliable and Intent-Preserving Code Translation with LLMs
Research Track
Shahriar Rumi Dipto University of Saskatchewan, Saikat Mondal University of Saskatchewan, Chanchal K. Roy University of Saskatchewan
Pre-print Media Attached File Attached
14:40
10m
Research paper
Leveraging Change Types and Contexts to Guide LLMs for Automated Test Code Updating
Research Track
Taicheng Huang Sun Yat-sen University, Xiangping Chen Sun Yat-sen University, Yuan Huang Sun Yat-sen University, Changlin Yang Sun Yat-sen University
Media Attached
14:50
10m
Talk
Automated Test Suite Enhancement Using Large Language Models with Few-shot Prompting
Research Track
Alex Chudic US Booking Services Ltd. (freetobook), Gül Calikli University of Glasgow
Pre-print File Attached
15:00
10m
Talk
Palm: Path-aware LLM-based Test Generation with Comprehension
Research Track
Yaoxuan Wu UCLA, Xiaojie Zhou UCLA, Ahmad Humayun Virginia Tech, Muhammad Ali Gulzar Virginia Tech, Miryung Kim UCLA and Amazon Web Services
Link to publication Media Attached
15:10
20m
Live Q&A
Joint QA and Discussion
ICPC Program