Extracting software product line feature models from natural language specifications

Sreekumar, A.; Planas, Elena; Clarisó, Robert

doi:10.1145/3233027.3233029

Cited by 22 publications

(8 citation statements)

References 24 publications

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“…Besides, we also found the use of OpenNLP and SharpNLP. Stanford's POS tagger, Brill, TreeTagger, dependency parser, [11], [17], [35] use case [37], [57] test case [35], [57] processed srs [58], [59] activity diagram [7], [60] meta model [57], [61] Other Outputs: traceability [44]; graph [62]; sbvr [63]; er-diagram [64]; processed named entity [65]; b-spec [66]; owl class [67]; proposal [68]; collaboration diagram [13]; test cases [57]; natural language [69]; feature diagram [70]; gui prototype [71]…”

Section: A Rq1: What Are the Existing Approaches To Automate The Uml ...mentioning

confidence: 99%

“…Studies aggregation [3]- [6], [12], [14]- [16], [24], [28], [29], [31], [37], [42], [43], [46], [64], [72], [75] association [4], [6], [11], [12], [17], [23], [24], [29]- [31], [37], [38], [43], [46], [65], [70], [72], [75], [76] generalization [3]- [6], [12], [15], [23], [24], [28], [29], [31], [37] composition [4], [12], [24], [28], [42], [43], [64], [76] multiplicity [6],…”

Section: Relation Resolutionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Ahmed,

Eisty

2022

Preprint

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Context: Processing Software Requirement Specifications (SRS) manually takes a much longer time for requirement analysts in software engineering. Researchers have been working on making an automatic approach to ease this task. Most of the existing approaches require some intervention from an analyst or are challenging to use. Some automatic and semi-automatic approaches were developed based on heuristic rules or machine learning algorithms. However, there are various constraints to the existing approaches of UML generation, such as restriction on ambiguity, length or structure, anaphora, incompleteness, atomicity of input text, requirements of domain ontology, etc. . Objective: This study aims to better understand the effectiveness of existing systems and provide a conceptual framework with further improvement guidelines. Method: We performed a systematic literature review (SLR). We conducted our study selection into two phases and selected 70 papers. We conducted quantitative and qualitative analyses by manually extracting information, cross-checking, and validating our findings. Result: We described the existing approaches and revealed the issues observed in these works. We identified and clustered both the limitations and benefits of selected articles. Conclusion: This research upholds the necessity of a common dataset and evaluation framework to extend the research consistently. It also describes the significance of natural language processing obstacles researchers face. In addition, it creates a path forward for future research.

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Section: A Rq1: What Are the Existing Approaches To Automate The Uml ...mentioning

confidence: 99%

Section: Relation Resolutionmentioning

confidence: 99%

Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Ahmed,

Eisty

2022

Preprint

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“…At the moment, there are few limitations in published researches on feature extraction from natural language documents, i.e. unavailable tools for evaluation, restricted or limited input, irrelevant feature naming, non-reproducible result, and domain engineer intervention in the process [32]. While this research is aimed to produce a tool for automatically extracting software features directly from SRS documents without any human intervention in the process.…”

Section: Natural Language Processing (Nlp) For Information Extractionmentioning

confidence: 99%

Automated Features Extraction from Software Requirements Specification (SRS) Documents as The Basis of Software Product Line (SPL) Engineering

Haris

Kurniawan

Ramdani

2020

JITeCS

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Extractive Software Product Line Engineering (SPLE) puts features on the foremost aspect in domain analysis that needs to be extracted from the existing system's artifact. Feature in SPLE, which is closely related to system functionality, has been previously studied to be extracted from source code, models, and various text documents that exist along the software development process. Source code, with its concise and normative standard, has become the most focus target for feature extraction source on many kinds of research. However, in the software engineering principle, the Software Requirements Specification (SRS) document is the basis or main reference for system functionality conformance. Meanwhile, previous researches of feature extraction from text document are conducted on a list of functional requirement sentences that have been previously prepared, not literally SRS as a whole document. So, this research proposes direct processing on the SRS document that uses requirement boilerplates for requirement sentence statement. The proposed method uses Natural Language Processing (NLP) approach on the SRS document. Sequence Part-of-Speech (POS) tagging technique is used for automatic requirement sentence identification and extraction. The features are acquired afterward from extracted requirement sentences automatically using the word dependency parsing technique. Besides, mostly the previous researches about feature extraction were using non-public available SRS document that remains classified or not accessible, so this work uses selected SRS from publicly available SRS dataset to add reproducible research value. This research proves that requirement sentence extraction directly from the SRS document is viable with precision value from 64% to 100% and recall value from 64% to 89%. While features extraction from extracted requirement sentences has success rate from 65% to 88%.

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“…In this section, we outline the most related work on extracting software features from these data and recommending them to designers. Because textual product descriptions mining can build the domain feature model 17,18 and user comments mining can identify new features desired by users, extracting software features from these textual data is of great interest to many researchers. [19][20][21] One kind of approaches tends to cluster the sentences of the textual data into different clusters by different methods.…”

Section: Related Workmentioning

confidence: 99%

Recommending software features to designers: From the perspective of users

et al. 2020

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With lots of public software descriptions emerging in the application market, it is significant to extract common software features from these descriptions and recommend them to new designers. However, existing approaches often recommend features according to their frequencies which reflect designers' preferences. In order to identify those users' favorite features and help design more popular software, this paper proposes to make use of the public data of users' ratings and products' downloads which reflect users' preferences to recommend extracted features. The proposed approach distinguishes users' perspective from designers' perspective and argues that users' perspective is better for recommending features because most products are designed for users and expect to be popular among users. Based on the lasso regression to estimate the relationship between the extracted features and the users' ratings, it first distinguishes the extracted features to identify those recommendable and undesirable features. By treating each download as a support from users to the product featurefeatures, it further mines the feature association rules from users' perspective for recommending features. By taking the public data on the market of SoftPedia.com for evaluation, our empirical studies indicate that: (i) selecting recommendable features by lasso regression is better than that by feature frequencies in terms of F 1 measure; and (ii) recommending features based on the feature association rules mined from users' perspective is not only feasible but also has competitive performance compared with that based on the rules mined from designs' perspective in terms of F 1 measure.

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Extracting software product line feature models from natural language specifications

Cited by 22 publications

References 24 publications

Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Automated Features Extraction from Software Requirements Specification (SRS) Documents as The Basis of Software Product Line (SPL) Engineering

Recommending software features to designers: From the perspective of users

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