| Title | : | Batch Alias Analysis |
| Speaker | : | Venkata Naga Jyothi V (IITM) |
| Details | : | Mon, 8 Jul, 2019 11:00 AM @ AM Turing Hall |
| Abstract: | : | Many program-analysis based tools require precise points-to or alias information only for some variables in the program. To meet this requirement efficiently, there have been many works on demand-driven analyses that perform only the work necessary to compute the points-to or alias information on the requested variables (queries). However, these demand-driven analyses can be very expensive when applied on large systems where the number of queries can be significant. Such a blow-up in analysis time is not acceptable in cases where scalability plays a crucial role, owing to real-time constraints; for example, when program analysis tools are plugged into an IDE (Interactive Development Environment). In this work, we propose schemes to improve the scalability of demand-driven analyses without compromising on their precision.
Our approach is based on novel ideas for eliminating the analysis of irrelevant and redundant data-flow paths with respect to the queries. We introduce the idea of batch analysis, which can answer multiple given queries in batch mode. Batch analysis suits the environments with strict time constraints, where the queries come in batch. We present a batch alias analysis framework that can be used to speed up given demand-driven alias analysis. To show the effectiveness of our batch analysis framework, we use two demand-driven alias analyses (1) the existing best performing demand-driven alias analysis tool for race-detection clients and (2) an optimized version thereof that avoids irrelevant computation. Our evaluations on a simulated data-race client, and on a recent program-understanding tool, show that batch analysis leads to significant performance gains, along with minor gains in precision. |
