LHDiff: A Language-Independent Hybrid Approach for Tracking Source Code Lines

Asaduzzaman, Muhammad; Roy, Chanchal K.; Schneider, Kevin A.; Penta, Massimiliano Di

doi:10.1109/icsm.2013.34

Cited by 37 publications

(20 citation statements)

References 29 publications

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“…Details about evaluation procedure and detailed results can be found in a separate paper [1]. In the following, we also highlight the benefits of using the simhash technique, and also warn users about the limitation of the tool.…”

Section: Performancementioning

confidence: 97%

“…It is a hybrid technique because it leverages findings collected through analyzing incorrect mappings of existing state-of-the-art location tracking techniques. The tool works in five different phases and they are briefly summarized below (further details about the LHDiff approach/algorithm can be found in a related research paper [1]):…”

Section: Lhdiff: Tracking Source Code Linesmentioning

confidence: 99%

“…Next, it computes the context and content similarity using source code lines to select one from each set of mapping candidates. Since the algorithm and its evaluation with other state-of-the-art line tracking techniques have been discussed in a separate paper [1], we briefly summarize the algorithm in this tool paper and explain tool syntax.…”

Section: Introductionmentioning

confidence: 99%

“…However, it has limitations in detecting reordered or changed lines that prevents it from becoming an ideal line tracking tool [3]. To address the problems, a number of line tracking techniques and tools have been developed [1].…”

Section: Introductionmentioning

confidence: 99%

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LHDiff: Tracking Source Code Lines to Support Software Maintenance Activities

Asaduzzaman

Roy

Schneider

et al. 2013

2013 IEEE International Conference on Software Maintenance

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Tracking lines across versions of a file is a necessary step for solving a number of problems during software development and maintenance. Examples include, but are not limited to, locating bug-inducing changes, tracking code fragments or vulnerable instructions across versions, co-change analysis, merging file versions, reviewing source code changes, and software evolution analysis. In this tool demonstration, we present a language-independent line-level location tracker, named LHDiff, that can be used to track lines and analyze changes in various kinds of software artifacts, ranging from source code to arbitrary text files. The tool can effectively detect changed or moved lines across versions of a file, has the ability to detect line splits, and can easily be integrated with existing version control systems. It overcomes the limitations of existing language-independent techniques and is even comparable to tools that are language dependent. In addition to describing the tool, we also describe its effectiveness in analyzing source code artifacts.

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Section: Performancementioning

confidence: 97%

Section: Lhdiff: Tracking Source Code Linesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LHDiff: Tracking Source Code Lines to Support Software Maintenance Activities

Asaduzzaman

Roy

Schneider

et al. 2013

2013 IEEE International Conference on Software Maintenance

Self Cite

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“…Quite a few papers track the origin of a single code line [3,5] with a similar goal-to provide accurate history information for metric calculations on the software's evolution. However, identifying changed source code lines cannot provide information for the more general problem of file origin analysis for two reasons.…”

Section: Line-based Origin Analysismentioning

confidence: 99%

Incremental origin analysis of source code files

Steidl¹,

Hummel²,

Juergens³

2014

Proceedings of the 11th Working Conference on Mining Software Repositories

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The history of software systems tracked by version control systems is often incomplete because many file movements are not recorded. However, static code analyses that mine the file history, such as change frequency or code churn, produce precise results only if the complete history of a source code file is available. In this paper, we show that up to 38.9% of the files in open source systems have an incomplete history, and we propose an incremental, commit-based approach to reconstruct the history based on clone information and name similarity. With this approach, the history of a file can be reconstructed across repository boundaries and thus provides accurate information for any source code analysis. We evaluate the approach in terms of correctness, completeness, performance, and relevance with a case study among seven open source systems and a developer survey.

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A parallel and efficient approach to large scale clone detection

Sajnani

Saini

Lopes

2015

J Software Evolu Process

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We propose a new token-based approach for large -scale code clone detection, which is based on a filtering heuristic that reduces the number of token comparisons when the two code blocks are compared. We also present a MapReduce based parallel algorithm that uses the filtering heuristic and scales to thousands of projects. The filtering heuristic is generic and can also be used in conjunction with other token-based approaches. In that context, we demonstrate how it can increase the retrieval speed and decrease the memory usage of the index-based approaches. In our experiments on 36 open source Java projects, we found that: (i) filtering reduces token comparisons by a factor of 10, and thus increasing the speed of clone detection by a factor of 1.5; (ii) the speed-up and scale-up of the parallel approach using filtering is nearlinear on a cluster of 2-32 nodes for 150-2800 projects; and (iii) filtering decreases the memory usage of index-based approach by half and the search time by a factor of 5.The presented approach is very general and can be used with other similarity function like Jaccard, Cosine, etc. 404HITESH SAJNANI, VAIBHAV SAINI AND CRISTINA LOPES Algorithm 5: Clone detection using efficient index-based Index search. Similar to the naive approach, given a query block b 1 , logically, detectClones() here also consists of the following two steps: (i) Fetch the candidate blocksthe terms in b 1 are first ordered using the globalTermPositionMap.Next, each term in the prefix of the ordered b 1 is searched in the partial index to retrieve the block ids of the candidate blocks. These block ids are added to candidatesList (line 21-30, Algorithm 5). It is important to note that unlike the naive approach, no similarity score is calculated here. This is because partial index does not index all the terms of the blocks, and similarity calculation requires all the terms of the candidate code block. In order to address this issue, we create another index that stores all the terms of a block id. We call this index as forward index because its purpose is exactly opposite to that of an inverted index. A forwardindex, when queried with a code block id, returns all the terms in that code block, whereas an 414 HITESH SAJNANI, VAIBHAV SAINI AND CRISTINA LOPES heuristic to improve index-based approaches. We demonstrated that filtering, indeed, can reduce the index size by half and decreases the search time by a factor of 5.5. Our parallel algorithm using filtering technique efficiently scales to thousands of projects and demonstrated near linear speed-up and scale-up. Moreover, its MapReduce based implementation has inherent advantages like load balancing, data replication, and fault tolerance over any other in-house distributed solutions where these things are to be dealt with explicitly. Support for replicating the study. We have made available the input dataset, tools, generated output, and the detailed steps to replicate the study at URL -http://mondego.ics.uci.edu/projects/ clonedetection. The web page has all the 36 subj...

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LHDiff: A Language-Independent Hybrid Approach for Tracking Source Code Lines

Cited by 37 publications

References 29 publications

LHDiff: Tracking Source Code Lines to Support Software Maintenance Activities

LHDiff: Tracking Source Code Lines to Support Software Maintenance Activities

Incremental origin analysis of source code files

A parallel and efficient approach to large scale clone detection

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