Towards Fault Localization via Probabilistic Software Modeling

Thaller, Hannes; Linsbauer, Lukas; Egyed, Alexander; Fischer, Stefan

doi:10.1109/vst50071.2020.9051635

Cited by 4 publications

(2 citation statements)

References 32 publications

(55 reference statements)

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“…Our future work focuses on constructing a comprehensive benchmark covering controlled and real-world systems for improved generalizability of clone detection studies. Furthermore, semantic clone detection has the potential to enable new methods for fault localization applications [42].…”

Section: Discussionmentioning

confidence: 99%

Semantic Clone Detection via Probabilistic Software Modeling

Thaller¹,

Linsbauer²,

Bladel³

et al. 2022

Fundamental Approaches to Software Engineering

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Semantic clone detection is the process of finding program elements with similar or equal runtime behavior. For example, detecting the semantic equality between the recursive and iterative implementation of the factorial computation. Semantic clone detection is the de facto technical boundary of clone detectors. In recent years, this boundary has been tested using interesting new approaches. This article contributes a semantic clone detection approach that detects clones which have 0 % syntactic similarity. We present Semantic Clone Detection via Probabilistic Software Modeling (SCD-PSM) as a stable and precise solution to semantic clone detection. PSM builds a probabilistic model of a program that is capable of evaluating and generating runtime data. SCD-PSM leverages this model and its model elements for finding behaviorally equal model elements. This behavioral equality is then generalized to semantic equality of the original program elements. It uses the likelihood between model elements as a distance metric. Then, it employs the likelihood ratio significance test to decide whether this distance is significant, given a pre-specified and controllable false-positive rate. The output of SCD-PSM are pairs of program elements (i.e., methods), their distance, and a decision on whether they are clones or not. SCD-PSM yields excellent results with a Matthews Correlation Coefficient greater than 0.9. These results are obtained on classical semantic clone detection problems such as detecting recursive and iterative versions of an algorithm, but also on complex problems used in coding competitions.

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Section: Discussionmentioning

confidence: 99%

Semantic Clone Detection via Probabilistic Software Modeling

Thaller¹,

Linsbauer²,

Bladel³

et al. 2022

Fundamental Approaches to Software Engineering

Self Cite

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“…In addition, there is a significant lack of studies showing the cost-benefit analysis of their proposed ML techniques, which would be vital for ML-based approaches to be feasible for adaptation in the industry. 190,191,192,193,194,195,196,197,198,199,200,201,202,203…”

Section: Applications Of ML Aiming At Software Maintenancementioning

confidence: 99%

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq¹,

Mashkoor

Mayr-Dorn

et al. 2021

IEEE Access

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The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

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