Method relocation refactorings, primarily Move Method and Pull Up/Push Down Method, are indispensable for reducing coupling and enhancing cohesion. Despite their widespread automation in modern refactoring engines, these algorithms remain notoriously error-prone, posing significant risks to software reliability. A prim...
Chun-Hao Dong, Yan-Jie Jiang, Yang Zhang et al.· 0 citations
Software debugging is a critical and time-consuming aspect of software development, with fault localization being a fundamental step that significantly impacts debugging efficiency. Mutation-Based Fault Localization (MBFL) has gained prominence due to its robust theoretical foundations and fine-grained analysis capabil...
Heng-Yuan Liu, Xia Song, Yong Liu et al.· 0 citations
Analyzing, understanding, and validating the performance of modern processors present significant challenges. These stem from two primary issues. First, it is difficult to construct “performance tests” that can test precisely scoped hypotheses about microarchitectural behavior. Second, it is difficult to make sense of...
Guo-Kai Chen, Sergi Soler Arrufat, Clément Pit-Claudel et al.· Proceedings of the ACM on Pr...· 0 citations
Neude is presented, a lightweight and extensible coverage-guided fuzzing framework specifically designed for hybrid AI-enabled systems that combines observations of program execution and neural model coverage to guide input mutations toward unexplored state spaces, enabling systematic testing of the entire hybrid syste...
Xin-Yu Gao, Yang Feng, Yu-Chen Lu et al.· Proceedings of the ACM on So...· 0 citations
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Test driven development (TDD) is a controversial and interesting approach to software development; while many think of better tests as a primary purpose of TDD, in practice the goal is as much to use tests to encourage continued progress in coding. That goal however rests on the notion that TDD ensures tests are good e...
A human-LLM workflow that pairs each curated semantic mutant with an instructor-approved seed phrase for an on-demand LLM expansion, which shows that the curated semantic-mutant set contains a fraction of the mutants a traditional mutation engine produces.
Rebecca Williams Earle, Jonathan Bell· Proceedings of the 2026 ACM...· 0 citations
SAT solving is a core computational challenge across hardware verification, electronic design, and planning; an FPGA can evaluate SAT candidates at hardware clock rates without software overhead, enabling sub-microsecond solving on small instances. Yet, the throughput-area tradeoffs of parallel FPGA SAT datapaths remai...
Andrew Bonilla· Companion Proceedings of the...· 0 citations
This proposed dissertation investigates mutation testing from three complementary perspectives: how mutations interact with program executions and test oracles to produce mutant kills and reveal opportunities for test improvement, and how mutation testing is adopted, configured, sustained, and acted upon in popular ope...
Hang Du· Companion Proceedings of the...· 0 citations
This talk explores how agent-first development platforms like Google Antigravity shift the paradigm from manual test authoring to autonomous software verification, and describes recent advances in autonomous visual testing.
Paige Bailey· Companion Proceedings of the...· 0 citations
PyMut4SE is a novel mutation tool for Python that focuses on a comprehensive set of mutations for any Python project, providing a rich and extensible set of mutation operators, access to mutated source code and its characteristics, detailed execution and behavioral observations, and support for both selective mutation...
Laura Plein, Matthieu Jimenez, Mike Papadakis· Companion Proceedings of the...· 0 citations
Memory-safety bugs are one of the oldest and most common sources of security vulnerabilities, and their modern-day prevalence is a consequence of the widespread use of non-memory-safe languages such as C and C++. Transitioning away from C and C++ to memory-safe languages, namely Rust, to eliminate memory-safety bugs is...
Victor Chen· Companion Proceedings of the...· 0 citations
Fuzzing finds bugs by testing software behavior with high volumes of input. To create semantically valid inputs for different targets, traditional fuzzers require significant engineering effort. LLM-based fuzzing approaches can be adapted to create inputs for a wide range of software but introduce new issues: every tes...
Lu Maltsis· Companion Proceedings of the...· 0 citations
“X-Hinges” helps design and 3D print items like lamps and gloves with an interactive twist: automatically embedded sensors that perceive and adaptively respond to human contact.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
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