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Une approche CSP pour l'aide \`a la localisation d'erreurs

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Mohammed Bekkouche, Hélène Collavizza, Michel Rueher

Artificial Intelligence · Submitted 25 Apr 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We present preliminary experimental results that are quite encouraging.

how it is stated · asserted 0.83 read the record →

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arXiv ID1404.6567 (version 1)
Pith IDYHUZG477, Pith's permanent record of this paper and its review
CitationH\'el\`ene Collavizza, Michel Rueher, Mohammed Bekkouche. "Une approche CSP pour l'aide \`a la localisation d'erreurs." arXiv:1404.6567 (2014). https://pith.science/paper/YHUZG477
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,883 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.13 · states limitation 0.44 · human participants 0.02 · controlled comparison 0.05 · randomized assignment 0.02 · states quantitative result 0.16 · compares with prior results 0.40 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We introduce in this paper a new CP-based approach to support errors location in a program for which a counter-example is available, i.e. an instantiation of the input variables that violates the post-condition. To provide helpful information for error location, we generate a constraint system for the paths of the CFG (Control Flow Graph) for which at most k conditional statements may be erroneous. Then, we calculate Minimal Correction Sets (MCS) of bounded size for each of these paths. The removal of one of these sets of constraints yields a maximal satisfiable subset, in other words, a maximal subset of constraints satisfying the post condition. We extend the algorithm proposed by Liffiton and Sakallah \cite{LiS08} to handle programs with numerical statements more efficiently. We present preliminary experimental results that are quite encouraging.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We introduce in this paper a new CP-based approach to support errors location in a program for which a counter-example is available, i.e. an instantiation of the input variables that violates the post-condition. contribution 1.00 0.93 0.22 asserted 1.00
s1 To provide helpful information for error location, we generate a constraint system for the paths of the CFG (Control Flow Graph) for which at most k conditional statements may be erroneous. method 0.98 0.92 0.05 asserted 0.99
s2 Then, we calculate Minimal Correction Sets (MCS) of bounded size for each of these paths. method 0.99 0.93 0.06 asserted 1.00
s3 The removal of one of these sets of constraints yields a maximal satisfiable subset, in other words, a maximal subset of constraints satisfying the post condition. method 0.79 0.69 0.09 asserted 0.99
s4 We extend the algorithm proposed by Liffiton and Sakallah \cite{LiS08} to handle programs with numerical statements more efficiently. contribution 0.74 0.86 0.04 asserted 1.00
s5 We present preliminary experimental results that are quite encouraging. result 0.76 0.91 0.25 asserted 0.83

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.91.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We introduce in this paper a new CP-based approach to support errors location in a program for which a counter-example is available, i.e. an instantiation of the input variables that violates the post-condition. To provide helpful information for error location, we generate a constraint system for the paths of the CFG (Control Flow Graph) for which at most k conditional statements may be erroneous. Then, we calculate Minimal Correction Sets (MCS) of bounded size for each of these paths. The removal of one of these sets of constraints yields a maximal satisfiable subset, in other words, a maximal subset of constraints satisfying the post condition. We extend the algorithm proposed by Liffiton and Sakallah \cite{LiS08} to handle programs with numerical statements more efficiently. We present preliminary experimental results that are quite encouraging.

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2 arXiv papers cite this in 2 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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