Dynamic symbolic data structure repair

Hussain, Ishtiaque; Csallner, Christoph

doi:10.1145/1810295.1810333

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…Sun et al propagate bug fixes to all applicable locations [30]. Hussain et al focus on the repair of complex data structures [15]. Lazaar et al automatically correct constraint programs [22].…”

Section: Discussion and Threats To Validitymentioning

confidence: 99%

R2Fix: Automatically Generating Bug Fixes from Bug Reports

Liu

Yang

Tan

et al. 2013

2013 IEEE Sixth International Conference on Software Testing, Verification and Validation

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Abstract-Many bugs, even those that are known and documented in bug reports, remain in mature software for a long time due to the lack of the development resources to fix them. We propose a general approach, R2Fix, to automatically generate bug-fixing patches from free-form bug reports. R2Fix combines past fix patterns, machine learning techniques, and semantic patch generation techniques to fix bugs automatically. We evaluate R2Fix on three projects, i.e., the Linux kernel, Mozilla, and Apache, for three important types of bugs: buffer overflows, null pointer bugs, and memory leaks. R2Fix generates 57 patches correctly, 5 of which are new patches for bugs that have not been fixed by developers yet. We reported all 5 new patches to the developers; 4 have already been accepted and committed to the code repositories. The 57 correct patches generated by R2Fix could have shortened and saved up to an average of 63 days of bug diagnosis and patch generation time.

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Section: Discussion and Threats To Validitymentioning

confidence: 99%

R2Fix: Automatically Generating Bug Fixes from Bug Reports

Liu

Yang

Tan

et al. 2013

2013 IEEE Sixth International Conference on Software Testing, Verification and Validation

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“…A similar approach has been proposed in the context automated data structure repair [15], [20], [16]. The rectification algorithm used in SOAP is inspired by the data structure repair algorithm proposed by Demsky et al [15], which iteratively modifies a data structure to enforce data consistency defined in an abstract model.…”

Section: Related Workmentioning

confidence: 99%

Automatic input rectification

Long¹,

Carbin²,

Sidiroglou³

et al. 2012

2012 34th International Conference on Software Engineering (ICSE)

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Abstract-We present a novel technique, automatic input rectification, and a prototype implementation called SOAP. SOAP learns a set of constraints characterizing typical inputs that an application is highly likely to process correctly. When given an atypical input that does not satisfy these constraints, SOAP automatically rectifies the input (i.e., changes the input so that is satisfies the learned constraints). The goal is to automatically convert potentially dangerous inputs into typical inputs that the program is highly likely to process correctly.Our experimental results show that, for a set of benchmark applications (namely, Google Picasa, ImageMagick, VLC, Swfdec, and Dillo), this approach effectively converts malicious inputs (which successfully exploit vulnerabilities in the application) into benign inputs that the application processes correctly. Moreover, a manual code analysis shows that, if an input does satisfy the learned constraints, it is incapable of exploiting these vulnerabilities.We also present the results of a user study designed to evaluate the subjective perceptual quality of outputs from benign but atypical inputs that have been automatically rectified by SOAP to conform to the learned constraints. Specifically, we obtained benign inputs that violate learned constraints, used our input rectifier to obtain rectified inputs, then paid Amazon Mechanical Turk users to provide their subjective qualitative perception of the difference between the outputs from the original and rectified inputs. The results indicate that rectification can often preserve much, and in many cases all, of the desirable data in the original input.

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“…To repair the erroneous output of the cycle faulty implementation, constraint-based repair methods [4,8,10] History-aware contract-based repair first invokes the SAT solver and tries to find a solution by only changing the values of the fields which the program writes into during the execution (Figure 2 (e)). These fields are distinguished by dotted lines in the faulty output.…”

Section: Examplementioning

confidence: 99%

“…As we discussed and showed in Section 4.5, the accuracy and efficiency of Juzi suffer for errors that omit nodes and because the repair does not consider method semantics at the entry and exit. Recent improvements include Dynamic Symbolic Data Structure (DSDS) repair which builds a symbolic representation of fields and objects along the repOK executed path [8]. Whenever a predicate fails, DSDS solves the conjunction of its negation with the other path conditions.…”

Section: Related Workmentioning

confidence: 99%

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History-Aware Data Structure Repair Using SAT

Zaeem

Gopinath

Khurshid

et al. 2012

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. Data structure repair corrects erroneous executions in deployed programs while they execute, eliminating costly downtime. Recent techniques show how to leverage specifications and a SAT solver to enforce specification conformance at runtime. While this powerful methodology increases the reliability of deployed programs, scalability remains a key technical challenge-satisfying a specification often results in the exploration of a huge state space. We present a novel technique, called history-aware contract-based repair for more efficient data structure repair using SAT. Our insight is two-fold: (1) the dynamic program trace of field writes and reads provides useful guidance to repair incorrect state mutations by a faulty program; and (2) we show how to execute SAT using unsatisfiable cores it generates, in an efficient iterative approach on successive problems with increasing state spaces, in order to utilize the history of previous runs as captured in the unsatisfiable core. We implement this approach in a new tool, called Cobbler, that repairs Java programs. Experimental results on two large applications and a library implementation of a linked list show that Cobbler significantly outperforms previous techniques for specification-based repair using SAT, and finds and repairs a previously undetected bug.

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Dynamic symbolic data structure repair

Cited by 10 publications

References 13 publications

R2Fix: Automatically Generating Bug Fixes from Bug Reports

R2Fix: Automatically Generating Bug Fixes from Bug Reports

Automatic input rectification

History-Aware Data Structure Repair Using SAT

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