Relevant bibliographies by topics / Clone Detection and Analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Clone Detection and Analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Clone Detection and Analysis"

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Mubarak-Ali, Al-Fahim, Rahiwan Nazar Romli, and Nilam Nur Amir Sjarif. "Code Clone Detection Model: A SWOT Analysis Perspective." Advanced Science Letters 24, no. 10 (October 1, 2018): 7210–13. http://dx.doi.org/10.1166/asl.2018.12916.

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Akhin, Marat, and Vladimir Itsykson. "Tree Slicing in Clone Detection: Syntactic Analysis Made (Semi)-Semantic." Modeling and Analysis of Information Systems 19, no. 6 (March 12, 2015): 69–78. http://dx.doi.org/10.18255/1818-1015-2012-6-69-78.

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Nowadays most of software contains code duplication that leads to serious problems in software maintenance. A lot of different clone detection approaches have been proposed over the years to deal with this problem, but almost all of them do not consider semantic properties of the source code. We propose to reinforce traditional tree-based clone detection algorithms by using additional information about variable slices. This allows to find intertwined/gapped clones on variables; preliminary evaluation confirms applicability of our approach to real-world software.
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Sotolongo, Ricardo, Fangyan Dong, and Kaoru Hirota. "Semantically Enhanced Code Clone Refinement Algorithm Based on Analysis of Multiple Detection Reports." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 3 (May 20, 2011): 322–28. http://dx.doi.org/10.20965/jaciii.2011.p0322.

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An algorithm based on semantic analysis of multiple detection tools’ reports using WordNet is proposed oriented on the refinement of code clones. It parses different detection tools’ reports looking for new clone specifications, and refines the location of existing ones using semantic information contained in source code. It is applied to a real and complex software system and is compared to three other well-known detection algorithms, discovering 4888 clone pairs more than the average detected by other tools; also making the code clones 3 lines longer (for a subset of the same system the results are proportional to the size reduction). The objective is to provide higher quantity of code clones, and more appropriated localization to be used in refactoring processes.
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Bartoszuk, Maciej, and Marek Gagolewski. "SimilaR: R Code Clone and Plagiarism Detection." R Journal 12, no. 1 (2020): 367. http://dx.doi.org/10.32614/rj-2020-017.

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Sargsyan, Sevak, Shamil Kurmnagaleev, Andrey Belevantsev, Hayk Aslanyan, and Artiom Baloian. "Scalable code clone detection tool based on semantic analysis." Proceedings of the Institute for System Programming of the RAS 27, no. 1 (2015): 39–50. http://dx.doi.org/10.15514/ispras-2015-27(1)-3.

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Lalar, Sacachin, Shashi Bhushan, and Surender Surender. "Analysis of Clone Detection Approaches in Static Wireless Sensor Networks." Oriental journal of computer science and technology 10, no. 3 (August 5, 2017): 653–59. http://dx.doi.org/10.13005/ojcst/10.03.14.

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Wireless Sensor Networks (WSNs) are developing very fast in the wireless networks. The wireless sensor network has the characteristics of limited memory, small size and limited battery. WSNs are vulnerable to the different types of attacks due to its characteristics. One of the attacks is clone node attack in which attacker capture the nodes from the network and stoles the information from it and replicates it in the network. From the clone nodes, the attacker can easily launch the different type of attacks in the network. To detect the clone node, different methods has been implemented .Each method having advantages and limitations. In the this paper, we explain the different methods to detect the clone nodes in the static wireless sensor network and compare their performance based on the communication cost and memory.
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Ma, Yu-Seung, and Duk-Kyun Woo. "Domain Analysis of Device Drivers Using Code Clone Detection Method." ETRI Journal 30, no. 3 (June 9, 2008): 394–402. http://dx.doi.org/10.4218/etrij.08.0107.0204.

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Haferlach, Claudia, Susanne Schnittger, Alice Fabarius, Armin Leitner, Wolfgang Kern, Ulrike Bacher, Rüdiger Hehlmann, Andreas Hochhaus, and Torsten Haferlach. "Analysis of Philadelphia Negative Clones Detected during Treatment with Tyrosine Kinase Inhibitors: A Study on 63 CML Cases." Blood 110, no. 11 (November 16, 2007): 4541. http://dx.doi.org/10.1182/blood.v110.11.4541.4541.

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Abstract The occurrence of Philadelphia chromosome negative (Ph−) clones in chronic myeloid leukemia (CML) patients treated with tyrosine kinase inhibitors have been reported in approximately 5% of cases. The pathogenesis of this phenomenon still remains unclear. The clinical relevance of these new clones remains to be clarified, as only occasional reports describe the presence of hematological dysplastic features or development of overt disease such as MDS or AML. We found 63 patients with CML that developed Ph− clones and performed in total 281 chromosome analyses (median: 4 analyses per case; range, 1–18). In total, 66 clonal abnormalities were detected. 60 cases showed only one aberration, in the remaining 3 cases 2 abnormalities were detected. Remarkably, no complex aberrant karyotypes were observed. Most frequent aberrations were gains and losses of whole chromosomes: +8 (n=35, 55.6%), +Y (n=3, 4.8%), +11 (n=2, 3.2%), +X (n=1, 1.6%), −Y (n=9, 14.3%), −7 (n=6, 9.5%). The following abnormalities were only observed in a single case: inv(Y)(p11.1q11.2); +1,der(1;7)(q10;p10); del(5)(q13q33); der(7)del(7)(p13)del(7)(q11.2); del(7)(q11q22); der(7;15)(q10;q10); t(8;11)(q22;q23); del(12)(p11p13); del(20)(q11q13). The majority of aberrations were unbalanced, only 2 balanced rearrangements were observed. No clonal evolution was found. Although this pattern of abnormalities resembles closest the pattern observed in MDS or Ph− chronic myeloproliferative disorder, only 1 case with −7 developed a MDS and subsequently an AML. Most frequently in addition to the Ph− clone a Ph+ clone and a normal clone was observed (n=86). In 8 analyses the Ph− clone was the only clone detected and in 60 analyses the Ph− clone was accompanied by a normal clone, in 10 by a Ph+ clone. In one case two different Ph− clones were observed during the course of the disease. For 34 patients detailed clinical data are available. All these patients were treated with imatinib, 7 patients subsequently received dasatinib and 3 nilotinib after imatinib treatment. The Ph− clone was observed after a median of 43 months (mo) after diagnosis and 20.5 mo after start of imatinib treatment, respectively. Dasatinib treatment was started 2, 3, 6, 10 and 12 mo prior to the first detection of the Ph− clone and in 1 case 5 mo after occurrence of the Ph− clone. Nilotinib treatment was started 6, 7 and 11 mo prior to the first detection of the Ph− clone. In 15 cases imatinib treatment was started within the first 4 mo after diagnosis. In these cases the Ph− clone was observed in median 13 mo after start of imatinib treatment (range: 4–64). Overall the 34 cases were monitored for 428 mo after occurrence of the Ph− clone (median=11.5 mo). In 6 cases the Ph− clone was lost during follow-up (in one case after allogeneic SCT). In conclusion: Ph− clones are stable over time in most cases. In the majority of cases only single, usually unbalanced abnormalities are observed. The size of the Ph− clone fluctuates and can also disappear. In most cases the Ph− clones seem to have no clinical impact. Longer follow-up is necessary to clarifiy the prognostic impact. So far the available data do not imply that the occurrence of Ph− clones per se should lead to changes in treatment strategy. However, close cytogenetic monitoring is recommended.
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Kaur, Gundeep, and Sumit Sharma. "Metric level based code clone detection using optimized code manager." International Journal of Engineering & Technology 7, no. 2.27 (August 6, 2018): 144. http://dx.doi.org/10.14419/ijet.v7i2.27.13763.

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Object-oriented programming today, is the main prototype in typical software development. Code Cloning defines generally, all through the designing and development of software systems. Detection can be based on Textual analysis, Lexical analysis, Syntax analysis, Semantic analysis, Hybrid analysis and Metric analysis. The major drawback of the present research is that it focuses more on fragments of copied code and does not focus on the aspect that the fragments of duplicated code are may be part of a larger replicated program structure. In this process, techniques take a lot of time and it creates complexity. In our research, a source code is then scanned for detecting various methods by adopting a “OPTIMIZED SVM ALGORITHM” and the method definitions are extracted and collected by means of a CLONE CODE and saved for further reference. To evaluate the performance parameters we calculate the LOC, the number of repetitions, and maximum and minimum LOC. To enhance the performance metrics precision recall, accuracy and reduce the error rate and time complexity
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Aktas, Mehmet S., and Mustafa Kapdan. "Structural Code Clone Detection Methodology Using Software Metrics." International Journal of Software Engineering and Knowledge Engineering 26, no. 02 (March 2016): 307–32. http://dx.doi.org/10.1142/s0218194016500133.

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Unnecessary repeated codes, also known as code clones, have not been well documented and are difficult to maintain. Code clones may become an important problem in the software development cycle, since any detected error must be fixed in all occurrences. This condition significantly increases software maintenance costs and requires effort/duration for understanding the code. This research introduces a novel methodology to minimize or prevent the code cloning problem in software projects. In particular, this manuscript is focused on the detection of structural code clones, which are defined as similarity in software structure such as design patterns. Our proposed methodology provides a solution to the class-level structural code clone detection problem. We introduce a novel software architecture that provides unification of different software quality analysis tools that take measurements for software metrics for structural code clone detection. We present an empirical evaluation of our approach and investigate its practical usefulness. We conduct a user study using human judges to detect structural code clones in three different open-source software projects. We apply our methodology to the same projects and compare results. The results show that our proposed solution is able to show high consistency compared with the results reached by the human judges. The outcome of this study also indicates that a uniform structural code clone detection system can be built on top of different software quality tools, where each tool takes measurements of different object-oriented software metrics.
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Dissertations / Theses on the topic "Clone Detection and Analysis"

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Krutz, Daniel Edward. "Code Clone Discovery Based on Concolic Analysis." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/203.

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Software is often large, complicated and expensive to build and maintain. Redundant code can make these applications even more costly and difficult to maintain. Duplicated code is often introduced into these systems for a variety of reasons. Some of which include developer churn, deficient developer application comprehension and lack of adherence to proper development practices. Code redundancy has several adverse effects on a software application including an increased size of the codebase and inconsistent developer changes due to elevated program comprehension needs. A code clone is defined as multiple code fragments that produce similar results when given the same input. There are generally four types of clones that are recognized. They range from simple type-1 and 2 clones, to the more complicated type-3 and 4 clones. Numerous clone detection mechanisms are able to identify the simpler types of code clone candidates, but far fewer claim the ability to find the more difficult type-3 clones. Before CCCD, MeCC and FCD were the only clone detection techniques capable of finding type-4 clones. A drawback of MeCC is the excessive time required to detect clones and the likely exploration of an unreasonably large number of possible paths. FCD requires extensive amounts of random data and a significant period of time in order to discover clones. This dissertation presents a new process for discovering code clones known as Concolic Code Clone Discovery (CCCD). This technique discovers code clone candidates based on the functionality of the application, not its syntactical nature. This means that things like naming conventions and comments in the source code have no effect on the proposed clone detection process. CCCD finds clones by first performing concolic analysis on the targeted source code. Concolic analysis combines concrete and symbolic execution in order to traverse all possible paths of the targeted program. These paths are represented by the generated concolic output. A diff tool is then used to determine if the concolic output for a method is identical to the output produced for another method. Duplicated output is indicative of a code clone. CCCD was validated against several open source applications along with clones of all four types as defined by previous research. The results demonstrate that CCCD was able to detect all types of clone candidates with a high level of accuracy. In the future, CCCD will be used to examine how software developers work with type-3 and type-4 clones. CCCD will also be applied to various areas of security research, including intrusion detection mechanisms.
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Anbalagan, Sindhuja. "On Occurrence Of Plagiarism In Published Computer Science Thesis Reports At Swedish Universities." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-5377.

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In recent years, it has been observed that software clones and plagiarism are becoming an increased threat for one?s creativity. Clones are the results of copying and using other?s work. According to the Merriam – Webster dictionary, “A clone is one that appears to be a copy of an original form”. It is synonym to duplicate. Clones lead to redundancy of codes, but not all redundant code is a clone.On basis of this background knowledge ,in order to safeguard one?s idea and to avoid intentional code duplication for pretending other?s work as if their owns, software clone detection should be emphasized more. The objective of this paper is to review the methods for clone detection and to apply those methods for finding the extent of plagiarism occurrence among the Swedish Universities in Master level computer science department and to analyze the results.The rest part of the paper, discuss about software plagiarism detection which employs data analysis technique and then statistical analysis of the results.Plagiarism is an act of stealing and passing off the idea?s and words of another person?s as one?s own. Using data analysis technique, samples(Master level computer Science thesis report) were taken from various Swedish universities and processed in Ephorus anti plagiarism software detection. Ephorus gives the percentage of plagiarism for each thesis document, from this results statistical analysis were carried out using Minitab Software.The results gives a very low percentage of Plagiarism extent among the Swedish universities, which concludes that Plagiarism is not a threat to Sweden?s standard of education in computer science.This paper is based on data analysis, intelligence techniques, EPHORUS software plagiarism detection tool and MINITAB statistical software analysis.
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Nilsson, Erik. "Abstract Syntax Tree Analysis for Plagiarism Detection." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-80888.

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Today, universities rely heavily on systems for detecting plagiarism in students’essays and reports. Code submissions however require specific tools. A numberof approaches to finding plagiarisms in code have already been tried, includingtechniques based on comparing textual transformations of code, token strings,parse trees and graph representations. In this master’s thesis, a new system, cojac,is presented which combines textual, tree and graph techniques to detect a broadspectrum of plagiarism attempts. The system finds plagiarisms in C, C++ and Adasource files. This thesis discusses the method used for obtaining parse trees fromthe source code and the abstract syntax tree analysis. For comparison of syntaxtrees, we generate sets of fingerprints, digest forms of trees, which makes thecomparison algorithm more scalable. To evaluate the method, a set of benchmarkfiles have been constructed containing plagiarism scenarios which was analyzedboth by our system and Moss, another available system for plagiarism detection incode. The results show that our abstract syntax tree analysis can effectively detectplagiarisms such as changing the format of the code and renaming of identifiersand is at least as effective as Moss for detecting plagiarisms of these kinds
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Elva, Rochelle. "Detecting Semantic Method Clones in Java Code using Method IOE-Behavior." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5731.

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The determination of semantic equivalence is an undecidable problem; however, this dissertation shows that a reasonable approximation can be obtained using a combination of static and dynamic analysis. This study investigates the detection of functional duplicates, referred to as semantic method clones (SMCs), in Java code. My algorithm extends the input-output notion of observable behavior, used in related work [1, 2], to include the effects of the method. The latter property refers to the persistent changes to the heap, brought about by the execution of the method. To differentiate this from the typical input-output behavior used by other researchers, I have coined the term method IOE-Behavior; which means its input-output and effects behavior [3]. Two methods are defined as semantic method clones, if they have identical IOE-Behavior; that is, for the same inputs (actual parameters and initial heap state), they produce the same output (that is result- for non-void methods, and final heap state). The detection process consists of two static pre-filters used to identify candidate clone sets. This is followed by dynamic tests that actually run the candidate methods, to determine semantic equivalence. The first filter groups the methods by type. The second filter refines the output of the first, grouping methods by their effects. This algorithm is implemented in my tool JSCTracker, used to automate the SMC detection process. The algorithm and tool are validated using a case study comprising of 12 open source Java projects, from different application domains and ranging in size from 2 KLOC (thousand lines of code) to 300 KLOC. The objectives of the case study are posed as 4 research questions: 1. Can method IOE-Behavior be used in SMC detection? 2. What is the impact of the use of the pre-filters on the efficiency of the algorithm? 3. How does the performance of method IOE-Behavior compare to using only input-output for identifying SMCs? 4. How reliable are the results obtained when method IOE-Behavior is used in SMC detection? Responses to these questions are obtained by checking each software sample with JSCTracker and analyzing the results. The number of SMCs detected range from 0 45 with an average execution time of 8.5 seconds. The use of the two pre-filters reduces the number of methods that reach the dynamic test phase, by an average of 34%. The IOE-Behavior approach takes an average of 0.010 seconds per method while the input-output approach takes an average of 0.015 seconds. The former also identifies an average of 32% false positives, while the SMCs identified using input-output, have an average of 92% false positives. In terms of reliability, the IOE-Behavior method produces results with precision values of an average of 68% and recall value of 76% on average. These reliability values represent an improvement of over 37% (for precision) of the values in related work [4]. Thus, it is my conclusion that IOE-Behavior can be used to detect SMCs in Java code with reasonable reliability.
Ph.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science
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Rieger, Matthias. "Effective clone detection without language barriers /." [S.l.] : [s.n.], 2005. http://www.zb.unibe.ch/download/eldiss/05rieger_m.pdf.

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Ersson, Sara. "Code Clone Detection for Equivalence Assurance." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284329.

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To support multiple programming languages, the concept of offering applicationprogramming interfaces (APIs) in multiple programming languages hasbecome commonplace. However, this also brings the challenge of ensuringthat the APIs are equivalent regarding their interface. To achieve this, codeclone detection techniqueswere adapted to match similar function declarationsin the APIs. Firstly, existing code clone detection tools were investigated. Asthey did not perform well, a tree-based syntactic approach was used, where allheader files were compiled with Clang. The abstract syntax trees, which wereobtained during the compilation, were then traversed to locate the functiondeclaration nodes, and to store function names and parameter variable names.When matching the function names, a textual approach was used, transformingthe function names according to a set of implemented rules.A strict rule compares transformations of full function names in a preciseway, whereas a loose rule only compares transformations of parts of functionnames, and matches anything for the remainder. The rules were appliedboth by themselves, and in different combinations, starting with the strictestrule, followed by the second strictest rule, and so fourth.The best-matching rules showed to be the ones which are strict, and are notaffected by the order of the functions in which they are matched. These rulesshowed to be very robust to API evolution, meaning an increase in number ofpublic functions. Rules which are less strict and stable, and not robust to APIevolution, can still be used, such as matching functions on the first or last wordin the function names, but preferably as a complement to the stricter and morestable rules, when most of the functions already have been matched.The tool has been evaluated on the two APIs in King’s software developmentkit, and covered 94% of the 124 available function matches.
För att stödja flera olika programmingsspråk har det blivit alltmer vanligt atterbjuda applikationsprogrammeringsgränssnitt (API:er) på olika programmeringsspråk.Detta resulterar dock i utmaningen att säkerställa att API:erna ärekvivalenta angående deras gränssnitt. För att uppnå detta har kodklonsdetekteringsteknikeranpassats, för att matcha liknande funktionsdeklarationeri API:erna. Först undersöktes existerande kodklonsverktyg. Eftersom de intepresterade bra, användes ett trädbaserat syntaktiskt tillvägagångssätt, där allaheader-filer kompilerades med Clang. De abstrakta syntaxträden, som erhöllsunder kompileringen, traverserades sedan för att lokalisera funktionsdeklarationsnoderna,och för att lagra funktionsnamnen och parametervariabelnamnen.När funktionsnamnen matchades, användes ett textbaserat tillvägagångssätt,som omvandlade funktionsnamnen enligt en uppsättning implementeraderegler.En strikt regel jämför omvandlingar av hela funktionsnamn på ett exakt sätt,medan en lös regel bara jämför omvandlingar av delar of funktionsnamn, ochmatchar den resterande delen med vadsomhelst. Reglerna applicerades bådasjälva och i olika kombinationer, där den striktaste regeln applicerades först,följt av den näst strikaste, och så vidare.De regler som matchar bäst visade sig vara de som är striktast, och som intepåverkas av ordningen på funktionerna i vilken de matchas. Dessa reglervisade sig vara väldigt robusta mot API-evolution, dvs. ett ökat antal publikafunktioner i API:erna. Regler som är mindre strikta och stabila, och interobusta mot API-evolution kan fortfarande användas, men helst som ett komplementtill de striktare och mer stabila reglerna, när de flesta av funktionernaredan har blivit matchade.Verktyget har evaluerats på de två API:erna i Kings mjukvaruutvecklarkit, ochtäckte 94% av de tillgängliga funktionsmatchningarna.
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Zhang, Xianpeng. "Software Clone Detection Basedon Context Information." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324959.

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Software clone detection is very promising and innovative within the industryfield. Existing mainstream clone detection techniques mainly focus ondetecting the similarity of source code itself, which makes them capable ofdetecting Type I and Type II clones (Type I clones are two identical codefragments except for variations in format and Type II clones are twostructurally identical code fragments except for variations in format). Butthey rarely pay attention to the relationship between codes. It becomes animportant research area to detect Type III code clones, which are clones withminor difference in statements, by using the context information in thesource code. I carry out a detailed analysis of existing software clone detectiontechniques in this thesis. It raises issues of existing software clone detectiontechniques in theory and practice. On the basis of the analysis, I propose anew method to improve existing clone detection techniques with a detailedtheory analysis and experimental verification. This method makes detectionof Type III software clones possible.
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Bahtiyar, Muhammed Yasin. "JClone: Syntax tree based clone detection for Java." Thesis, Linnaeus University, School of Computer Science, Physics and Mathematics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-5455.

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An unavoidable amount of money is spent on maintaining existing software systems today. Software maintenance cost generally higher than development cost of the system therefore lowering maintenance cost is highly appreciated in software industry.

A significant part of maintenance activities is related to repeating the investigation of problems and applying repeated solutions several times. A software system may contain a common bug in several different places and it might take extra effort and time to fix all existences of this bug. This operation commonly increases the cost of Software Maintenance Activities.

Detecting duplicate code fragments can significantly decrease the time and effort therefore the maintenance cost. Clone code detection can be achieved via analyzing the source code of given software system. An abstract syntax tree based clone detector for java systems is designed and implemented through this study.

This master thesis examines a software engineering process to create an abstract syntax tree based clone detector for the projects implemented in Java programming language.

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Khan, Mohammed Salman. "A Topic Modeling approach for Code Clone Detection." UNF Digital Commons, 2019. https://digitalcommons.unf.edu/etd/874.

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In this thesis work, the potential benefits of Latent Dirichlet Allocation (LDA) as a technique for code clone detection has been described. The objective is to propose a language-independent, effective, and scalable approach for identifying similar code fragments in relatively large software systems. The main assumption is that the latent topic structure of software artifacts gives an indication of the presence of code clones. It can be hypothesized that artifacts with similar topic distributions contain duplicated code fragments and to prove this hypothesis, an experimental investigation using multiple datasets from various application domains were conducted. In addition, CloneTM, an LDA-based working prototype for code clone detection was developed. Results showed that, if calibrated properly, topic modeling can deliver a satisfactory performance in capturing different types of code clones, showing particularity good performance in detecting Type III clones. CloneTM also achieved levels of performance comparable to already existing practical tools that adopt different clone detection strategies.
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Saini, Vaibhav Pratap Singh. "Towards Accurate and Scalable Clone Detection Using Software Metrics." Thesis, University of California, Irvine, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10981732.

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Code clone detection tools find exact or similar pieces of code, known as code clones. Code clones are categorized into four types of increasing difficulty of detection, ranging from purely textual (Type I) to purely semantic (Type IV). Most clone detectors reported in the literature, work well up to Type III, which accounts for syntactic differences. In between Type III and Type IV, however, there lies a spectrum of clones that, although still exhibiting some syntactic similarities, are extremely hard to detect—the Twilight Zone. Besides correctness, scalability has become a must-have requirement for modern clone detection tools. The increase in amount of source code in web-hosted open source repository services has presented opportunities to improve the state of the art in various modern use cases of clone detection such as detecting similar mobile applications, license violation detection, mining library candidates, code repair, and code search among others. Though these opportunities are exciting, scaling such vast corpora poses critical challenge.

Over the years, many clone detection techniques and tools have been developed. One class of these techniques is based on software metrics. Metrics based clone detection has potential to identify clones in the Twilight Zone. For various reasons, however, metrics-based techniques are hard to scale to large datasets. My work highlights issues which prohibit metric based clone detection techniques to scale large datasets while maintaining high levels of correctness. The identification of these issues allowed me to rethink how metrics could be used for clone detection.

This dissertation starts by presenting an empirical study using software metrics to understand if metrics can be used to identify differences in cloned and non-cloned code. The study is followed by another large scale study to explore the extent of cloning in GitHub. Here, the dissertation highlights scalability challenges in clone detection and how they were addressed. The above two studies provided a strong base to use software metrics for clone detection in a scalable manner. To this end, the dissertation presents Oreo, a novel approach 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. This dissertation evaluates the recall of Oreo on BigCloneBench, a benchmark of real world code clones. In experiments to compare the detection performance of Oreo with other five state of the art clone detectors, we found that 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. Further, to address the issues identified in precision evaluations, the dissertation presents InspectorClone, a semi automated approach to facilitate precision studies of clone detection tools. InspectorClone makes use of some of the concepts introduced in the design of Oreo to automatically resolve different types of clone pairs. Experiments demonstrate that InspectorClone has a very high precision and it significantly reduces the number of clone pairs that need human validation during precision experiments. Moreover, InspectorClone aggregates the individual effort of multiple teams into a single evolving dataset of labeled clone pairs, creating an important asset for software clone research. Finally, the dissertation concludes with a discussion on the lessons learned during the design and development of Oreo and lists down a few areas for the future work in code clone detection.

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Books on the topic "Clone Detection and Analysis"

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Inoue, Katsuro, and Chanchal K. Roy, eds. Code Clone Analysis. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1927-4.

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McCarthy, Anne. Methods of analysis and detection. Cambridge: Cambridge University Press, 1997.

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Mohanta, Abhijit, and Anoop Saldanha. Malware Analysis and Detection Engineering. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6193-4.

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Olympia, Hadjiliadis, ed. Quickest detection. Cambridge: Cambridge University Press, 2009.

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A, Busch Marianna, ed. Multielement detection systems for spectrochemical analysis. New York: Wiley, 1990.

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Dressel, Ralph K. Detection and analysis of proper names. Manchester: UMIST, 1997.

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N, Ngan King, ed. Digital video transition analysis and detection. River Edge, N.J: World Scientific, 2002.

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Voigtman, Edward. Limits of Detection in Chemical Analysis. Hoboken, New Jersey: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119189008.

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Heng, Wei Jyh. Digital video transition analysis and detection. Singapore: World Scientific Publishing, 2004.

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PC viruses: Detection, analysis, and cure. London: Springer-Verlag, 1991.

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Book chapters on the topic "Clone Detection and Analysis"

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Saini, Vaibhav, Farima Farmahinifarahani, Hitesh Sajnani, and Cristina Lopes. "Oreo: Scaling Clone Detection Beyond Near-Miss Clones." In Code Clone Analysis, 63–74. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1927-4_5.

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Krinke, Jens, and Chaiyong Ragkhitwetsagul. "Code Similarity in Clone Detection." In Code Clone Analysis, 135–50. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1927-4_10.

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Sajnani, Hitesh, Vaibhav Saini, Chanchal K. Roy, and Cristina Lopes. "SourcererCC: Scalable and Accurate Clone Detection." In Code Clone Analysis, 51–62. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1927-4_4.

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Li, Liuqing, He Feng, Na Meng, and Barbara Ryder. "CCLearner: Clone Detection via Deep Learning." In Code Clone Analysis, 75–89. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1927-4_6.

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Mondal, Manishankar, Chanchal K. Roy, and James R. Cordy. "NiCad: A Modern Clone Detector." In Code Clone Analysis, 45–50. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1927-4_3.

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Mahajan, Ginika. "Efficiency and Precision Enhancement of Code Clone Detection Using Hybrid Technique-Based Web Tool." In Asset Analytics, 225–34. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9585-8_19.

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López, Juan, María Fuciños, Xosé R. Fdez-Vidal, and Xosé M. Pardo. "Detection and Matching of Lines for Close-Range Photogrammetry." In Pattern Recognition and Image Analysis, 732–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_87.

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Casado-García, Ángela, César Domínguez, Jónathan Heras, Eloy Mata, and Vico Pascual. "The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images." In Document Analysis Systems, 199–215. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57058-3_15.

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Alrabaee, Saed, Mourad Debbabi, Paria Shirani, Lingyu Wang, Amr Youssef, Ashkan Rahimian, Lina Nouh, Djedjiga Mouheb, He Huang, and Aiman Hanna. "Clone Detection." In Advances in Information Security, 187–209. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34238-8_8.

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Conti, Mauro. "Clone Detection." In Secure Wireless Sensor Networks, 75–100. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-3460-7_4.

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Conference papers on the topic "Clone Detection and Analysis"

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Brixtel, Romain, Mathieu Fontaine, Boris Lesner, Cyril Bazin, and Romain Robbes. "Language-Independent Clone Detection Applied to Plagiarism Detection." In 2010 10th IEEE Working Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 2010. http://dx.doi.org/10.1109/scam.2010.19.

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Su, Fang-Hsiang, Jonathan Bell, and Gail Kaiser. "Challenges in Behavioral Code Clone Detection." In 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER). IEEE, 2016. http://dx.doi.org/10.1109/saner.2016.75.

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Fördős, Viktória, and Melinda Tóth. "Comprehensible presentation of clone detection results." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4912559.

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Golubev, Yaroslav, Viktor Poletansky, Nikita Povarov, and Timofey Bryksin. "Multi-threshold token-based code clone detection." In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2021. http://dx.doi.org/10.1109/saner50967.2021.00053.

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Farmahinifarahani, Farima, Vaibhav Saini, Di Yang, Hitesh Sajnani, and Cristina V. Lopes. "On Precision of Code Clone Detection Tools." In 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2019. http://dx.doi.org/10.1109/saner.2019.8668015.

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Niu, Haofei, Tianchang Yang, and Shaozhang Niu. "Clone analysis and detection in android applications." In 2016 3rd International Conference on Systems and Informatics (ICSAI). IEEE, 2016. http://dx.doi.org/10.1109/icsai.2016.7811010.

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Wang, Cong, Jian Gao, Yu Jiang, Zhenchang Xing, Huafeng Zhang, Weiliang Yin, Ming Gu, and Jiaguang Sun. "Go-clone: graph-embedding based clone detector for Golang." In ISSTA '19: 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3293882.3338996.

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Vislavski, Tijana, Gordana Rakic, Nicolas Cardozo, and Zoran Budimac. "LICCA: A tool for cross-language clone detection." In 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2018. http://dx.doi.org/10.1109/saner.2018.8330250.

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van Bladel, Brent, and Serge Demeyer. "Clone Detection in Test Code: An Empirical Evaluation." In 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2020. http://dx.doi.org/10.1109/saner48275.2020.9054798.

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Dang, Yingnong, Dongmei Zhang, Song Ge, Ray Huang, Chengyun Chu, and Tao Xie. "Transferring Code-Clone Detection and Analysis to Practice." In 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP). IEEE, 2017. http://dx.doi.org/10.1109/icse-seip.2017.6.

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Reports on the topic "Clone Detection and Analysis"

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Eather, Robert H., and Ronald Siewert. Space Debris Detection and Analysis. Fort Belvoir, VA: Defense Technical Information Center, February 1994. http://dx.doi.org/10.21236/ada282012.

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Glover, J. M. Void Detection using Standing Wave Analysis. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1992. http://dx.doi.org/10.4095/133649.

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Kegelmeyer, W. Philip, Jeremy D. Wendt, and Ali Pinar. An Example of Counter-Adversarial Community Detection Analysis. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1481570.

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Castleberry, K. N. High-vibration detection using motor current signature analysis. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/366465.

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Eick, Brian, Zachary Treece, Billie Spencer, Matthew Smith, Steven Sweeney, Quincy Alexander, and Stuart Foltz. Miter gate gap detection using principal component analysis. Engineer Research and Development Center (U.S.), June 2018. http://dx.doi.org/10.21079/11681/27365.

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Erbacher, Robert F., and Robinson Pino. Open Source Software Tools for Anomaly Detection Analysis. Fort Belvoir, VA: Defense Technical Information Center, April 2014. http://dx.doi.org/10.21236/ada599306.

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Davis, Larry, and Ross Cutler. Real-Time Periodic Motion Detection, Analysis and Application. Fort Belvoir, VA: Defense Technical Information Center, July 1999. http://dx.doi.org/10.21236/ada391942.

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Butzbaugh, Joshua, Abraham SD Tidwell, and Chrissi Antonopoulos. Automatic Fault Detection & Diagnostics: Residential Market Analysis. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1670423.

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Matsumoto, David, Hyisung C. Hwang, Adam M. Fullenkamp, and C. M. Laurent. Human Deception Detection from Whole Body Motion Analysis. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ada626755.

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Robinson, David Gerald. Statistical language analysis for automatic exfiltration event detection. Office of Scientific and Technical Information (OSTI), April 2010. http://dx.doi.org/10.2172/983675.

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