Synergetic Interaction Between Fault Localization and Defect Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @PhdThesis{Sohn:thesis,
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author = "Jeongju Sohn",
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title = "Synergetic Interaction Between Fault Localization and
Defect Prediction",
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school = "COINSE Lab, School of Computing, Korea Advanced
Institute of Science and Technology",
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year = "2021",
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address = "Daejeon, 34141, Republic of Korea",
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month = "9 " # apr,
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keywords = "genetic algorithms, genetic programming, Genetic
Improvement, SBSE, search based software engineering,
fault localisation, defect prediction, SVM, random
forrest, FLUCCS, EMF, Ensemble Model for Fault
Localisation, Spectrum Based Fault Localisation, SBFL,
SAP HANA2",
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URL = "https://coinse.github.io/coinse/2021/04/09/Jeongju-Sohn-PhD-defense.html",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Sohn_thesis.pdf",
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size = "131 pages",
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abstract = "Identifying the root cause of a program failure (i.e.,
fault) is crucial for maintaining software quality.
Fault localisation intends to identify faults after
they are revealed through a program failure, whereas
defect prediction aims to predict yet-to-happen faults.
Although they both aim to identify faults in code, just
with different timing, fault localisation and defect
prediction have been mainly studied as separate
research topics, and thereby the synergy between them
remains largely under-explored. This thesis argues that
the synergetic interactions between fault localisation
and defect prediction can enhance both techniques, as
they share the common goal of identifying faulty code.
To validate this claim, we first investigate whether
defect prediction can improve fault localisation and
vice-versa. The empirical results show that fault
localisation and defect prediction do enhance each
other: leveraging code and change features widely
studied in defect prediction allows at least 22percent
more fault to be precisely localised, whereas using the
code suspiciousness computed from past fault
localisation can improve both the accuracy and the
actionability of defect prediction. Reducing the
debugging cost is crucial for industrial software
systems, as it directly affects the profit of a
company. Nevertheless, existing fault localisation
techniques expected to reduce this cost have been
rarely evaluated with industrial software systems. This
thesis also includes the industrial scale case study of
automated fault localisation techniques. The results of
the case study show that existing fault localisation
techniques can assist the debugging process of
industrial projects by finding faults the original
approach in the projects was unable to locate.",
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notes = "Supervisor: Shin Yoo",
- }
Genetic Programming entries for
Jeongju Sohn
Citations