Cause Clue Clauses: Error Localization using Maximum Satisfiability

TL;DR

This paper introduces a MAX-SAT-based algorithm for localizing error causes in programs by analyzing execution traces, effectively identifying minimal fault-inducing code segments.

Contribution

The paper presents a novel MAX-SAT reduction approach for error localization, implemented in the bug-assist tool for C programs, demonstrating high accuracy and efficiency.

Findings

Bug-assist accurately isolates fault-inducing code lines

The approach quickly identifies minimal causes of errors

Effective in suggesting fixes for common errors like off-by-one

Abstract

Much effort is spent everyday by programmers in trying to reduce long, failing execution traces to the cause of the error. We present a new algorithm for error cause localization based on a reduction to the maximal satisfiability problem (MAX-SAT), which asks what is the maximum number of clauses of a Boolean formula that can be simultaneously satisfied by an assignment. At an intuitive level, our algorithm takes as input a program and a failing test, and comprises the following three steps. First, using symbolic execution, we encode a trace of a program as a Boolean trace formula which is satisfiable iff the trace is feasible. Second, for a failing program execution (e.g., one that violates an assertion or a post-condition), we construct an unsatisfiable formula by taking the trace formula and additionally asserting that the input is the failing test and that the assertion condition does hold at the end. Third, using MAX-SAT, we find a maximal set of clauses in this formula that can be satisfied together, and output the complement set as a potential cause of the error. We have implemented our algorithm in a tool called bug-assist for C programs. We demonstrate the surprising effectiveness of the tool on a set of benchmark examples with injected faults, and show that in most cases, bug-assist can quickly and precisely isolate the exact few lines of code whose change eliminates the error. We also demonstrate how our algorithm can be modified to automatically suggest fixes for common classes of errors such as off-by-one.