Identifying syntactic differences between two programs

Yang, Wuu

doi:10.1002/spe.4380210706

Cited by 284 publications

(177 citation statements)

References 28 publications

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“…Additionally, TMDIFF supports elements without identity. In that case, TMDIFF performs a structural diff on the containment hierarchy (see, e.g., [20]). …”

Section: Discussion and Related Workmentioning

confidence: 99%

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Origin Tracking $$+$$ Text Differencing $$=$$ Textual Model Differencing

Rozen

Storm

2015

Theory and Practice of Model Transformations

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Abstract. In textual modeling, models are created through an intermediate parsing step which maps textual representations to abstract model structures. Therefore, the identify of elements is not stable across different versions of the same model. Existing model differencing algorithms, therefore, cannot be applied directly because they need to identify model elements across versions. In this paper we present Textual Model Diff (TMDIFF), a technique to support model differencing for textual languages. TMDIFF requires origin tracking during text-to-model mapping to trace model elements back to the symbolic names that define them in the textual representation. Based on textual alignment of those names, TMDIFF can then determine which elements are the same across revisions, and which are added or removed. As a result, TMDIFF brings the benefits of model differencing to textual languages.

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“…Additionally, TMDIFF supports elements without identity. In that case, TMDIFF performs a structural diff on the containment hierarchy (see, e.g., [20]). …”

Section: Discussion and Related Workmentioning

confidence: 99%

“…Note that the anonymous Transition objects lack such labels. In this case, the objects do not have an identity, and the difference algorithm will perform structural differencing (e.g., [20]), instead of semantic, model-based differencing [1]. Figure 2 shows two additional versions of the state machine of Fig.…”

Section: Overviewmentioning

confidence: 99%

Origin Tracking $$+$$ Text Differencing $$=$$ Textual Model Differencing

Rozen

Storm

2015

Theory and Practice of Model Transformations

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“…We use a simple tree matching algorithm introduced in [16] which was first proposed to compare two computer programs in software engineering. It calculates the similarity of two trees by finding the best match through dynamic programming with complexity O(n 1 n 2 ), where n 1 is the size (number of nodes) of T and n 2 is the size of T .…”

Section: Sibling Table Recognitionmentioning

confidence: 99%

Automatic Hidden-Web Table Interpretation by Sibling Page Comparison

Tao¹,

Embley²

2007

Conceptual Modeling - ER 2007

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Abstract. The longstanding problem of automatic table interpretation still illudes us. Its solution would not only be an aid to table processing applications such as large volume table conversion, but would also be an aid in solving related problems such as information extraction and semi-structured data management. In this paper, we offer a conceptual modeling solution for the common special case in which so-called sibling pages are available. The sibling pages we consider are pages on the hidden web, commonly generated from underlying databases. We compare them to identify and connect nonvarying components (category labels) and varying components (data values). We tested our solution using more than 2,000 tables in source pages from three different domains-car advertisements, molecular biology, and geopolitical information. Experimental results show that the system can successfully identify sibling tables, generate structure patterns, interpret tables using the generated patterns, and automatically adjust the structure patterns, if necessary, as it processes a sequence of hidden-web pages. For these activities, the system was able to achieve an overall F-measure of 94.5%.

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“…In this work, we use a restricted tree matching algorithm, called simple tree matching (STM) (Yang 1991). STM evaluates the similarity of two trees by producing the maximum matching through dynamic programming with complexity O(n 1 n 2 ), where n 1 and n 2 are the sizes of trees A and B respectively.…”

Section: Fig 2: the Overall Net Algorithmmentioning

confidence: 99%

NET – A System for Extracting Web Data from Flat and Nested Data Records

Liu

Zhai

2005

Lecture Notes in Computer Science

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Abstract. This paper studies automatic extraction of structured data from Web pages. Each of such pages may contain several groups of structured data records. Existing automatic methods still have several limitations. In this paper, we propose a more effective method for the task. Given a page, our method first builds a tag tree based on visual information. It then performs a post-order traversal of the tree and matches subtrees in the process using a tree edit distance method and visual cues. After the process ends, data records are found and data items in them are aligned and extracted. The method can extract data from both flat and nested data records. Experimental evaluation shows that the method performs the extraction task accurately.

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Identifying syntactic differences between two programs

Cited by 284 publications

References 28 publications

Origin Tracking $$+$$ Text Differencing $$=$$ Textual Model Differencing

Origin Tracking $$+$$ Text Differencing $$=$$ Textual Model Differencing

Automatic Hidden-Web Table Interpretation by Sibling Page Comparison

NET – A System for Extracting Web Data from Flat and Nested Data Records

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