Browsing by Author "Zheng, Guolong"

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    ATR: Template-based repair for alloy specifications
    (2022) Zheng, Guolong; Vu Nguyen, Thanh; Gutiérrez Brida, Simón; Regis, Germán; Aguirre, Nazareno; Frías, Marcelo F.; Bagheri, Hamid
    "Automatic Program Repair (APR) is a practical research topic that studies techniques to automatically repair programs to fix bugs. Most existing APR techniques are designed for imperative programming languages, such as C and Java, and rely on analyzing correct and incorrect executions of programs to identify and repair suspicious statements. We introduce a new APR approach for software specifications written in the Alloy declarative language, where specifications are not “executed”, but rather converted into logical formulas and analyzed using backend constraint solvers, to find specification instances and counterexamples to assertions. We present ATR, a technique that takes as input an Alloy specification with some violated assertion and returns a repaired specification that satisfies the assertion. The key ideas are (i) analyzing the differences between counterexamples that do not satisfy the assertion and instances that do satisfy the assertion to guide the repair and (ii) generating repair candidates from specific templates and pruning the space of repair candidates using the counterexamples and satisfying instances. Experimental results using existing large Alloy benchmarks show that ATR is effective in generating difficult repairs. ATR repairs 66.3% of 1974 fault specifications, including specification repairs that cannot be handled by existing Alloy repair techniques."
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    Bounded exhaustive search of alloy specification repairs
    (2021) Gutiérrez Brida, Simón; Regis, Germán; Zheng, Guolong; Bagher, Hamid; Nguyen, Thanh Vu; Aguirre, Nazareno; Frías, Marcelo
    "The rising popularity of declarative languages and the hard to debug nature thereof have motivated the need for applicable, automated repair techniques for such languages. However, despite significant advances in the program repair of imperative languages, there is a dearth of repair techniques for declarative languages. This paper presents BeAFix, an automated repair technique for faulty models written in Alloy, a declarative language based on first-order relational logic. BeAFix is backed with a novel strategy for bounded exhaustive, yet scalable, ex ploration of the spaces of fix candidates and a formally rigorous, sound pruning of such spaces. Moreover, different from the state of-the-art in Alloy automated repair, that relies on the availability of unit tests, BeAFix does not require tests and can work with assertions that are naturally used in formal declarative languages. Our experience with using BeAFix to repair thousands of real world faulty models, collected by other researchers, corroborates its ability to effectively generate correct repairs and outperform the state-of-the-art."
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    FLACK: Counterexample-guided fault localization for alloy models
    (2021) Zheng, Guolong; Nguyen, Thanh Vu; Gutiérrez Brida, Simón; Regis, Germán; Frías, Marcelo; Aguirre, Nazareno; Bagher, Hamid
    "Fault localization is a practical research topic that helps developers identify code locations that might cause bugs in a program. Most existing fault localization techniques are designed for imperative programs (e.g., C and Java) and rely on analyzing correct and incorrect executions of the program to identify suspicious statements. In this work, we introduce a fault localization approach for models written in a declarative language, where the models are not “executed,” but rather converted into a logical formula and solved using backend constraint solvers. We present FLACK, a tool that takes as input an Alloy model consisting of some violated assertion and returns a ranked list of suspicious expressions contributing to the assertion violation. The key idea is to analyze the differences between counterexamples, i.e., instances of the model that do not satisfy the assertion, and instances that do satisfy the assertion to find suspicious expressions in the input model. The experimental results show that FLACK is efficient (can handle complex, real world Alloy models with thousand lines of code within 5 seconds), accurate (can consistently rank buggy expressions in the top 1.9% of the suspicious list), and useful (can often narrow down the error to the exact location within the suspicious expressions)."