ORBS and the limits of static slicing

Binkley, David; Gold, Nicolas; Harman, Mark; Islam, Syed; Krinke, Jens; Yoo, Shin

doi:10.1109/scam.2015.7335396

Cited by 28 publications

(16 citation statements)

References 52 publications

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“…However, static analysis, even when construed as a conservative (i.e., 'safe') approach, can also yield false negatives. For example, a static slicing algorithm is only conservative with respect to a set of assumptions, and these assumptions always allow some false negatives; dependencies that exist between elements of real systems, yet which go undetected by the slicing algorithm [12].…”

Section: False Positives and Pseudo False Positivesmentioning

confidence: 99%

Deploying Search Based Software Engineering with Sapienz at Facebook

Alshahwan

Gao

Harman

et al. 2018

Search-Based Software Engineering

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We describe the deployment of the Sapienz Search Based Software Engineering (SBSE) testing system. Sapienz has been deployed in production at Facebook since September 2017 to design test cases, localise and triage crashes to developers and to monitor their fixes. Since then, running in fully continuous integration within Facebook's production development process, Sapienz has been testing Facebook's Android app, which consists of millions of lines of code and is used daily by hundreds of millions of people around the globe. We continue to build on the Sapienz infrastructure, extending it to provide other software engineering services, applying it to other apps and platforms, and hope this will yield further industrial interest in and uptake of SBSE (and hybridisations of SBSE) as a result. This paper was written to accompany the keynote by Mark Harman at the 10 th Symposium on Search-Based Software Engineering (SSBSE 2018), Montpellier September 8-10, 2018. The paper represents the work of all the authors in realising the deployment of search based approaches to large-scale software engineering at Facebook. Author name order is alphabetical; the order is thus not intended to denote any information about the relative contribution of each author.

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Section: False Positives and Pseudo False Positivesmentioning

confidence: 99%

Deploying Search Based Software Engineering with Sapienz at Facebook

Alshahwan

Gao

Harman

et al. 2018

Search-Based Software Engineering

Self Cite

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“…Work on automatic inference of statically correct assertions [72], has demonstrated that static truth about program computation (in the form of assertions that hold for all executions), can be inferred from a surprisingly small sample of input-output pairs, in a surprisingly large number of cases. The observation that a small amount of dynamic information can yield static truth has also been found in other software engineering domains [73].…”

Section: F Search Based Software Engineeringmentioning

confidence: 57%

Genetic Improvement of Software: A Comprehensive Survey

Petke

Haraldsson

Harman

et al. 2018

IEEE Trans. Evol. Computat.

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170

121

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Genetic improvement uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of genetic improvement back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. Genetic improvement has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between genetic improvement and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields.Index Terms-genetic improvement, survey 1089-778X (c)

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“…Prior work (Binkley et al, 2014) compared ORBS with various forms of dynamic slicing, all of which are its 'algorithmic cousins' because they all have common roots in dynamic analysis. Subsequently, ORBS slices were compared to static slices in order to explore the subtleties and limits of static analysis (Binkley et al, 2015). This paper directly compares the two implementations of observational slicing, ORBS and T-ORBS, "head-to-head".…”

Section: Research Questionsmentioning

confidence: 99%

A comparison of tree- and line-oriented observational slicing

et al. 2019

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Observation-based slicing and its generalization observational slicing are recentlyintroduced, language-independent dynamic slicing techniques. They both construct slices based on the dependencies observed during program execution, rather than static or dynamic dependence analysis. The original implementation of the observation-based slicing algorithm used lines of source code as its program representation. A recent variation, developed to slice modelling languages (such as Simulink), used an XML representation of an executable model. We ported the XML slicer to source code by constructing a tree representation of traditional source code through the use of srcML. This work compares the tree-and line-based slicers using four experiments involving twenty different programs, ranging from classic benchmarks to million-line production systems. The resulting slices are essentially the same size for the majority of the programs and are often identical. However, structural constraints imposed by the tree representation sometimes force the slicer to retain enclosing control structures. It can also "bog down" trying to delete single-token subtrees. This occasionally makes the tree-based slices larger and the tree-based slicer slower than a parallelised version of the line-based slicer. In addition, a Java versus C comparison finds that the two languages lead to similar slices, but Java code takes noticeably longer to slice. The initial experiments suggest two improvements to the tree-based slicer: the addition of a size threshold, for ignoring small subtrees, and subtree replacement. The former enables the slicer to run 3.4 times faster while producing slices that are only about 9% larger. At the same time the subtree replacement reduces size by about 8-12% and allows the tree-based slicer to produce more natural slices.

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ORBS and the limits of static slicing

Cited by 28 publications

References 52 publications

Deploying Search Based Software Engineering with Sapienz at Facebook

Deploying Search Based Software Engineering with Sapienz at Facebook

Genetic Improvement of Software: A Comprehensive Survey

A comparison of tree- and line-oriented observational slicing

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