Identifying duplicate functionality in textual use cases by aligning semantic actions

Rago, Alejandro; Marcos, Claudia; Díaz-Pace, J. Andrés

doi:10.1007/s10270-014-0431-3

Cited by 21 publications

(14 citation statements)

References 27 publications

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“…Rago et al [15] apply natural language processing and machine learning techniques to the problem of searching duplicate functionality in requirement specifications. These documents are considered as a set of textual use cases; the approach extracts sequence chain (usage scenarios) for every use case and compares the pairs of chains to find duplicate subchains.…”

Section: Related Workmentioning

confidence: 99%

Detecting Near Duplicates in Software Documentation

Luciv

Koznov

Chernishev

et al. 2018

Program Comput Soft

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Contemporary software documentation is as complicated as the software itself. During its lifecycle, the documentation accumulates a lot of "near duplicate" fragments, i.e. chunks of text that were copied from a single source and were later modified in different ways. Such near duplicates decrease documentation quality and thus hamper its further utilization. At the same time, they are hard to detect manually due to their fuzzy nature. In this paper we give a formal definition of near duplicates and present an algorithm for their detection in software documents. This algorithm is based on the exact software clone detection approach: the software clone detection tool Clone Miner was adapted to detect exact duplicates in documents. Then, our algorithm uses these exact duplicates to construct near ones. We evaluate the proposed algorithm using the documentation of 19 open source and commercial projects. Our evaluation is very comprehensive -it covers various documentation types: design and requirement specifications, programming guides and API documentation, user manuals. Overall, the evaluation shows that all kinds of software documentation contain a significant number of both exact and near duplicates. Next, we report on the performed manual analysis of the detected near duplicates for the Linux Kernel Documentation. We present both quantative and qualitative results of this analysis, demonstrate algorithm strengths and weaknesses, and discuss the benefits of duplicate management in software documents.

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Section: Related Workmentioning

confidence: 99%

Detecting Near Duplicates in Software Documentation

Luciv

Koznov

Chernishev

et al. 2018

Program Comput Soft

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“…This is an extended abstract of the journal article with the same name, for the MODELS 2015 Conference [1].…”

Section: Introductionmentioning

confidence: 99%

Identifying duplicate functionality in textual use cases by aligning semantic actions (SoSyM abstract)

Rago

Marcos

Díaz-Pace

2015

2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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Developing high-quality requirements specifications often demands a thoughtful analysis and an adequate level of expertise from analysts. Although requirements modeling techniques provide mechanisms for abstraction and clarity, fostering the reuse of shared functionality (e.g., via UML relationships for use cases), they are seldom employed in practice. A particular quality problem of textual requirements, such as use cases, is that of having duplicate pieces of functionality scattered across the specifications. Duplicate functionality can sometimes improve readability for end users, but hinders development-related tasks such as effort estimation, feature prioritization and maintenance, among others. Unfortunately, inspecting textual requirements by hand in order to deal with redundant functionality can be an arduous, time-consuming and error-prone activity for analysts. In this context, we introduce a novel approach called ReqAligner that aids analysts to spot signs of duplication in use cases in an automated fashion. To do so, ReqAligner combines several text processing techniques, such as a use-case-aware classifier and a customized algorithm for sequence alignment. Essentially, the classifier converts the use cases into an abstract representation that consists of sequences of semantic actions, and then these sequences are compared pairwise in order to identify action matches, which become possible duplications. We have applied our technique to five real-world specifications, achieving promising results and identifying many sources of duplication in the use cases.

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“…Given the detailed information provided by our scenario analysis approach (e.g., it indicates the source of the defect), we have developed a rule-based heuristic as part of our scenario analysis approach in order to recommend fixes to requirements engineers, so that they can review scenario descriptions and deal with defects that hurt the properties related to unambiguity, completeness and consistency via refactoring of scenarios. A similar strategy based on recommendation tables was proposed by Rago et al (2014).…”

Section: Recommending Fixes For Defectsmentioning

confidence: 99%

“…Parsing strategy returns a parse tree based on statistical analysis of POS tags; however, POS tagging strategies do not performs this task with high precision, such as demonstrated in Table 27. Table 27 shows the POS tagging results returned by Stanford (2015), NLTK (2015) and Compendium-js (2015) In Table 27, it is possible to notice that Stanford (2015) and NLTK (2015) tools did not identify the main verbs of three sentences. These verbs are tagged as "Nouns": "Process", "Downloads" and "Types".…”

Section: Phrase-structure Parsingmentioning

confidence: 99%

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Analysis of Natural Language Scenarios

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Identifying duplicate functionality in textual use cases by aligning semantic actions

Cited by 21 publications

References 27 publications

Detecting Near Duplicates in Software Documentation

Detecting Near Duplicates in Software Documentation

Identifying duplicate functionality in textual use cases by aligning semantic actions (SoSyM abstract)

Analysis of Natural Language Scenarios

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