Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Model-based development is becoming an increasingly common development methodology. In important domains like embedded systems already major parts of the code are generated from models specified with domain-specific modelling languages. Hence, such models are nowadays an integral part of the software development and maintenance process and therefore have a major economic and strategic value for the software-developing organisations. Nevertheless almost no work has been done on a quality defect that is known to seriously hamper maintenance productivity in classic code-based development: Cloning. This paper presents an approach for the automatic detection of clones in large models as they are used in model-based development of control systems. The approach is based on graph theory and hence can be applied to most graphical data-flow languages. An industrial case study demonstrates the applicability of our approach for the detection of clones in Matlab/Simulink models that are widely used in model-based development of embedded systems in the automotive domain.

Clone Detection in Automotive Model-Based Development.

Source code clones are categorized into four types of increasing difficulty of detection, ranging from purely textual (Type-1) to purely semantic (Type-4). Most clone detectors reported in the literature work well up to Type-3, which accounts for syntactic differences. In between Type-3 and Type-4, however, there lies a spectrum of clones that, although still exhibiting some syntactic similarities, are extremely hard to detect – the Twilight Zone. Most clone detectors reported in the literature fail to operate in this zone. We present Oreo, a novel approach to source code clone detection that not only detects Type-1 to Type-3 clones accurately, but is also capable of detecting harder-to-detect clones in the Twilight Zone. Oreo is built using a combination of machine learning, information retrieval, and software metrics. We evaluate the recall of Oreo on BigCloneBench, and perform manual evaluation for precision. Oreo has both high recall and precision. More importantly, it pushes the boundary in detection of clones with moderate to weak syntactic similarity in a scalable manner

Oreo: detection of clones in the twilight zone

No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading statistical methods in bioinformatics an introduction is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.

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Statistical Methods in Bioinformatics: An Introduction

If two fragments of source code are identical or similar to each other, they are called code clones. Code clones introduce difficulties in software maintenance and cause bug propagation. Software clones occur due to several reasons such as code reuse by copying pre-existing fragments, coding style, and repeated computation using duplicated functions with slight changes in variables or data structures used. If a code fragment is edited, it will have to be checked against all related code clones to see if they need to be modified as well. Removal, avoidance or refactoring of cloned code are other important issues in software maintenance. However, several research studies have demonstrated that removal or refactoring of cloned code is sometimes harmful. In this study, code clones, common types of clones, phases of clone detection, the state-ofthe-art in code clone detection techniques and tools, and challenges faced by clone detection techniques are discussed.

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A Survey of Software Clone Detection Techniques

If two fragments of source code are identical to each other, they are called code clones. Code clones introduce difficulties in software maintenance and cause bug propagation. In this paper, we present a machine learning framework to automatically detect clones in software, which is able to detect Types-3 and the most complicated kind of clones, Type-4 clones. Previously used traditional features are often weak in detecting the semantic clones The novel aspects of our approach are the extraction of features from abstract syntax trees (AST) and program dependency graphs (PDG), representation of a pair of code fragments as a vector and the use of classification algorithms. The key benefit of this approach is that our approach can find both syntactic and semantic clones extremely well. Our evaluation indicates that using our new AST and PDG features is a viable methodology, since they improve detecting clones on the IJaDataset 2.0.

Semantic Clone Detection Using Machine Learning

Code obfuscation is a staple tool in malware creation where code fragments are altered substantially to make them appear different from the original, while keeping the semantics unaffected. A majority of the obfuscated code detection methods use program structure as a signature for detection of unknown codes. They usually ignore the most important feature, which is the semantics of the code, to match two code fragments or programs for obfuscation. Obfuscated code detection is a special case of the semantic code clone detection task. We propose a detection framework for detecting both code obfuscation and clone using machine learning. We use features extracted from Java bytecode dependency graphs (BDG), program dependency graphs (PDG) and abstract syntax trees (AST). BDGs and PDGs are two representations of the semantics or meaning of a Java program. ASTs capture the structural aspects of a program. We use several publicly available code clone and obfuscated code datasets to validate the effectiveness of our framework. We use different assessment parameters to evaluate the detection quality of our proposed model. Experimental results are excellent when compared with contemporary obfuscated code and code clone detectors. Interestingly, we achieve 100% success in detecting obfuscated code based on recall, precision, and F1-Score. When we compare our method with other methods for all of obfuscations types, viz, contraction, expansion, loop transformation and renaming, our model appears to be the winner. In case of clone detection our model achieve very high detection accuracy in comparison to other similar detectors.

http://dspace.cus.ac.in/jspui/bitstream/1/6409/1/AbdullahSheneamerExpertSystems2017.pdf

A detection framework for semantic code clones and obfuscated code

If two fragments of source code are identical to each other, they are called code clones. Code clones introduce difficulties in software maintenance and cause bug propagation. Coarse-grained clone detectors have higher precision than fine-grained, but fine-grained detectors have higher recall than coarse-grained. In this paper, we present a hybrid clone detection technique that first uses a coarse-grained technique to analyze clones effectively to improve precision. Subsequently, we use a fine-grained detector to obtain additional information about the clones and to improve recall. Our method detects Type-1 and Type-2 clones using hash values for blocks, and gapped code clones (Type-3) using block detection and subsequent comparison between them using Levenshtein distance and Cosine measures with varying thresholds.

http://ieeexplore.ieee.org/iel7/7387214/7397173/07397263.pdf

Code clone detection using coarse and fine-grained hybrid approaches

To assist developers refactored code and to enable improvements to software quality when numbers of clones are found in software programs, we require an approach to advise developers on what a clone needs to refactor and what type of refactoring is needed This paper suggests a unique learning method that automatically extracts features from the detected code clones and trains models to advise developers on what type needs to be refactored Our approach differs from others, which specifies types of refactored clones as classes and creates a model for detecting the types of refactored clones and the clones which are anonymous We introduce a new method by which to convert refactoring clone type outliers into Unknown clone set to improve classification results We present an extensive comparative study and an evaluation of the efficacy of our suggested idea by using state-of-the-art classification models

/pdf/an-automatic-advisor-for-refactoring-software-clones-based-53d1vufk09.pdf

Abdullah Sheneamer

Papers

A Survey of Software Clone Detection Techniques

Semantic Clone Detection Using Machine Learning

A detection framework for semantic code clones and obfuscated code

Code clone detection using coarse and fine-grained hybrid approaches

An Automatic Advisor for Refactoring Software Clones Based on Machine Learning