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Found 9 papers, 5 papers with code
ChatDBG: An AI-Powered Debugging Assistant
Our ChatDBG prototype integrates with standard debuggers including LLDB, GDB, and WinDBG for native code and Pdb for Python.
CoverUp: Coverage-Guided LLM-Based Test Generation
This paper presents CoverUp, a novel system that drives the generation of high-coverage Python regression tests via a combination of coverage analysis and large-language models (LLMs).
Mossad: Defeating Software Plagiarism Detection
Mossad is both fast and effective: it can, in minutes, generate modified versions of programs that are likely to escape detection.
Scalene: Scripting-Language Aware Profiling for Python
Scalene employs a combination of sampling, inference, and disassembly of byte-codes to efficiently and precisely attribute execution time and memory usage to either Python, which developers can optimize, or library code, which they cannot.
Programming Languages Software Engineering
Tea: A High-level Language and Runtime System for Automating Statistical Analysis
We show that Tea generally matches the choices of experts while automatically switching to non-parametric tests when parametric assumptions are not met.
Programming Languages Human-Computer Interaction Mathematical Software Software Engineering
Mesh: Compacting Memory Management for C/C++ Applications
Programs written in C/C++ can suffer from serious memory fragmentation, leading to low utilization of memory, degraded performance, and application failure due to memory exhaustion.
Programming Languages Data Structures and Algorithms Performance
On the Impact of Programming Languages on Code Quality
This paper is a reproduction of work by Ray et al. which claimed to have uncovered a statistically significant association between eleven programming languages and software defects in projects hosted on GitHub.
Software Engineering
Coz: Finding Code that Counts with Causal Profiling
Guided by Coz, we improve the performance of Memcached by 9%, SQLite by 25%, and accelerate six PARSEC applications by as much as 68%; in most cases, these optimizations involve modifying under 10 lines of code.
Performance D.4.8; C.4
DoubleTake: Fast and Precise Error Detection via Evidence-Based Dynamic Analysis
The key insight of evidence-based dynamic analysis is that for a class of errors, it is possible to ensure that evidence that they happened at some point in the past remains for later detection.
Software Engineering D.2.5; D.2.4; D.3.4
