VLDB 2026 Research / reviewers in the wild / expert
Abhishek Rose
dblp:280/5106
· DBLP profile ↗
3ranked-venue papers
1as first author
2since 2021 · last 2024
0009-0002-2222-8906ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 3 · 1 first-author · 2 since 2021Systems, architecture and hardware · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Modeling Dynamic (De)Allocations of Local Memory for Translation ValidationabstractEnd-to-End Translation Validation is the problem of verifying the executable code generated by a compiler against the corresponding input source code for a single compilation. This becomes particularly hard in the presence of dynamically-allocated local memory where addresses of local memory may be observed by the program. In the context of validating the translation of a C procedure to executable code, a validator needs to tackle constant-length local arrays, address-taken local variables, address-taken formal parameters, variable-length local arrays, procedure-call arguments (including variadic arguments), and the alloca () operator. We provide an execution model, a definition of refinement, and an algorithm to soundly convert a refinement check into first-order logic queries that an off-the-shelf SMT solver can handle efficiently. In our experiments, we perform blackbox translation validation of C procedures (with up to 100+ SLOC), involving these local memory allocation constructs, against their corresponding assembly implementations (with up to 200+ instructions) generated by an optimizing compiler with complex loop and vectorizing transformations. Abhishek Rose, Sorav Bansal |
Proc. ACM Program. Lang. | 1 |
| 2022 | Automatic Generation of Debug Headers through BlackBox Equivalence CheckingabstractModern compiler optimization pipelines are large and complex, and it is rather cumbersome and error-prone for compiler developers to preserve debugging information across optimization passes. An optimization can add, remove, or reorder code and variables, which makes it difficult to associate the generated code statements and values with the source code statements and values. Moreover recent proposals for automatic generation of optimizations (e.g., through search algorithms) have not previously considered the preservation of debugging information.We demonstrate the application of a blackbox equivalence checker to automatically populate the debugging information in the debug headers of the optimized executables compiled from C programs. A blackbox equivalence checker can automatically compute equivalence proofs between the original source code and the optimized executable code without the knowledge of the exact transformations performed by the compiler/optimizer. We present an algorithm that uses these formal equivalence proofs to improve the executable’s debugging headers. We evaluate this approach on benchmarks derived from the Testsuite of Vectorizing Compilers (TSVC) compiled through three different compilers: GCC, ClangLLVM, and ICC. We demonstrate significant improvements in the debuggability of the optimized executable code in these experiments. The benefits of these improvements can be transparently realized through any standard debugger, such as GDB, to debug the updated executable. Vaibhav Kiran Kurhe, Pratik Karia, Shubhani Gupta, Abhishek Rose, Sorav Bansal |
CGO | 4 |
| 2020 | Counterexample-guided correlation algorithm for translation validationabstractAutomatic translation validation across the unoptimized intermediate representation (IR) of the original source code and the optimized executable assembly code is a desirable capability, and has the potential to compete with existing approaches to verified compilation such as CompCert. A difficult subproblem is the automatic identification of the correlations across the transitions between the two programs' respective locations. We present a counterexample-guided algorithm to identify these correlations in a robust and scalable manner. Our algorithm has both theoretical and empirical advantages over prior work in this problem space. Shubhani Gupta, Abhishek Rose, Sorav Bansal |
Proc. ACM Program. Lang. | 2 |