Automated Patch Extraction via Syntax- and Semantics-Aware Delta Debugging on Source Code Changes (ESEC/FSE2018)

Delta debugging (DD) is an approach to automating the debugging activities based on systematic testing. DD algorithms find the cause of a regression of a program by minimizing the changes applied between a working version and a faulty version of the program. However, it is still an open problem to minimize a huge set of […]

An Empirical Study of Computation-Intensive Loops for Identifying and Classifying Loop Kernels (ICPE2017)

The process of performance tuning is time consuming and costly even if it is carried out automatically. It is crucial to learn from the experience of experts. Our long-term goal is to construct a database of facts extracted from specific performance tuning histories of computation-intensive applications such that we can search the database for promising […]

Extracting Facts from Performance Tuning History of Scientific Applications for Predicting Effective Optimization Patterns (MSR2015)

To improve performance of large-scale scientific applications, scientists or tuning experts make various empirical attempts to change compiler options, program parameters or even the syntactic structure of programs. Those attempts followed by performance evaluation are repeated until satisfactory results are obtained. On account of combinatorial explosion of possible attempts, the task of performance tuning requires […]