Automatic repair of regular expressions

Abstract

We introduce RFixer, a tool for repairing complex regular expressions using examples. Given an incorrect regular expression and sets of positive and negative examples, RFixer synthesizes the closest regular expression to the original one that is consistent with the examples. Automatically repairing regular expressions requires exploring a large search space because practical regular expressions: i) are large, ii) operate over very large alphabetsÐe.g., UTF-16 and ASCIIÐand iii) employ complex constructsÐe.g., character classes and numerical quantifiers. RFixer's repair algorithm achieves scalability by taking advantage of structural properties of regular expressions to effectively prune the search space, and it employs satisfiability modulo theory solvers to efficiently and symbolically explore the sets of possible character classes and numerical quantifiers. RFixer could successfully compute minimal repairs for regular expressions collected from a variety of sources, whereas existing tools either failed to produce any repair or produced overly complex repairs. CCS Concepts: • Software and its engineering → General programming languages; • Theory of computation → Regular languages.

Multi-modal program inference: a marriage of pre-trained language models and component-based synthesis

“…A task is considered successfully done, if the output expression is semantically equivalent to the ground-truth of that task. We used an off-the-shelf tool, RFixer [Pan et al 2019], for deciding if two expressions are semantically equivalent. If the 6 presents the accuracy of nlx-reg and the baseline systems when applied on the DeepRegex (6a) and on the StackOverflow (6b) data-sets.…”

TransRegex: Multi-modal Regular Expression Synthesis by Generate-and-Repair

“…There are various drawbacks or limitations in existing repair techniques, such as only supporting positive or negative ex amples [26], [27], not supporting regexes with the conjunction (&) operator, or having the problems of under-fitting/overfitting [20], [28], To overcome these drawbacks or limitations, we present a novel and efficient algorithm, Sy n C o r r , based on Neighborhood Search (NS) to repair an incorrect regex to achieve that the repaired regex is consistent with the examples. Particularly Sy n C o r r alleviates the under-fitting/over-fitting problem, via preserving the integrity of the small sub-regexes.…”

“…Then Sy n Co r r calls the function transformations to get the neighbours of r, i.e., some abstract regexes (e.g., <VOW><S> (<NUM>) <Qr ><S>) that input an incorrect regex r0: ([AE10Uaeiou],*[0 -9],*){7,} r <-preprocessor^, = 0): (< V 0 W X S X N U M > < S > )< Q 7 > JV(r) «-trans form at ions (?) : In addition, it is worth noting that on the same example and the incorrect regex mentioned above, the repair tool RFix e r [28] produces the incorrect regex ( [AEeiO01234U56789] . * [0 -9 ] .…”

Ensuring the Correctness of Regular Expressions: A Review

“…Therefore, to repair a faulty regular expression, a new set of negative examples are required and the goal is to modify the original expression so that it rejects the new examples. On the contrary, Rebele [84] focused on repairing regular expressions that define languages smaller than the intended ones. Thus, a new set of positive examples is provided, and the goal is to modify the original expression so that it accepts the new examples.…”

“…To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Testing status [14] RE coverage based [33] From positive examples [52] Syntax check [10] Highlight [72−74] Based on positive examples [83] Bug classification [22] DFA coverage based [11] From both examples [53−57] Semantic check [68−70] Automaton graph [75−79] Based on negative examples [84] Feature usage [8] Mutation based [49] Gold′s learning [59−61] Verification [71] Tree view [74,80] Based on both examples [85] Evolution [24] Sampling [40,41] From specifications [62−64] Visual IDE [81] Based on genetic programming [88,89] Comprehension [23] Enumerating [91] From specifications and examples [66] Visual structure [82] Interactive repair [90] Composition [25] Random [45,47] Abstraction [12] Repair vulnerable [86,87] Portability [28] Systematic [46−48] Development cycle [30] Determinism [32] Readability [92]…”