Debuggimine

Debuggimine is a software engineering concept representing an automated debugging framework designed to locate, reproduce, and repair defects in modern software systems. The term refers to both the methodology and its reference implementation used in research and some industry contexts. The name is a portmanteau of debug and mining, signaling a data-driven approach to defect recovery.

Origin and development: Debuggimine was first described in 2018 by researchers including Dr. Elena Morin at a conference on automated program repair. Since then it has appeared in scholarly discussions as a general concept rather than a single commercial product, serving as a framework for combining analysis, synthesis, and verification techniques.

Core components and workflow: The framework combines static analysis, dynamic tracing, and machine learning to localize bugs, propose patches, and validate them. Typical steps include instrumenting the codebase to collect traces, performing fault localization, generating repair candidates with program-synthesis techniques, and applying patches that are then regression-tested and, when possible, formally verified.

Applications and impact: Debuggimine is aimed at large-scale codebases, distributed systems, and safety-critical domains where debugging cycles are lengthy. It is used to accelerate debugging, improve reproducibility, and assist developers with patch ranking and explanation, thereby reducing time-to-fix in complex environments.

Limitations: It is not a universal solution; effectiveness depends on data quality, test coverage, and the ability to constrain the repair space. Patches must be reviewed to avoid regressions, and there is a risk of overfitting patches to observed failures.

See also: Related topics include automated program repair, symbolic execution, and delta debugging.