- author = "David Binkley and Nicolas Gold and Syed Islam and Jens Krinke and Shin Yoo",
- title = "A comparison of tree- and line-oriented observational slicing",
- journal = "Empirical Software Engineering",
- year = "2019",
- volume = "24",
- pages = "3077--3113",
- month = oct,
- note = "Special Section on Source Code Analysis and Manipulation",
- keywords = "genetic algorithms, genetic programming, ORBS, source code deletion, srcML, XML",
- ISSN = "1382-3256",
-
URL = "
http://www.cs.ucl.ac.uk/staff/j.krinke/publications/emse19.pdf",
-
DOI = "
doi:10.1007/s10664-018-9675-9",
- size = "41 pages",
- abstract = "Observation-based slicing and its generalization observational slicing are recently-introduced, 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 modeling 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 9percent larger. At the same time the subtree replacement reduces size by about 8 to 12 percent and allows the tree-based slicer to produce more natural slices.",
-
notes = "Not on GP but interesting manipulation of human
written high level code via tree based XML ?
Section 3.1.2 Subtree Replacement, similar to Hoist mutation \cite{kinnear:kinnear} ?
Section 6.2.2 X=10 over fitting?
srcML used in genetic improvement PyGGI 2.0 eg \cite{an:2019:fse}",
