Recommending Features and Feature Relationships from Requirements Documents for Software Product Lines

Hamza, Mostafa; Walker, Robert J.

doi:10.1109/raise.2015.12

Cited by 17 publications

(9 citation statements)

References 11 publications

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“…The recommender system developed by Hamza and Walker [39] applied several heuristics, such as actor-actionobject tree construction, to suggest features for Software Product Lines (SPLs) based on the manually extracted functional requirements. Its preprocessing stage included: PoS tagging to these sections, stop words removal, sorting, and deletion of duplicates.…”

Section: A Preliminary Clusteringmentioning

confidence: 99%

“…In GQM-based metrics recommender systems we can not classify or cluster textual data reliably [32], [44], [49], [50], [52], [60], as a virtually infinite number of metrics can be conceived. It can also be difficult to construct some heuristics [39]), or a huge number of pre-arranged questions and goals [62], [63]. The authors of the last papers succeeded in their goals because the number of existing patterns was limited (under 20).…”

Section: B Rq2: Which Algorithms Underline Recommender Systems For So...mentioning

confidence: 99%

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Technologies for GQM-Based Metrics Recommender Systems: A Systematic Literature Review

et al. 2022

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Purpose: With this Systematic Literature Review (SLR), we aim to discover technologies to construct a Goal-Question-Metrics (GQM) based metrics recommender for software developers. Since such a system has not yet been described in the literature, we decided to analyse the technologies used in three main components of recommender systems -data sets, algorithms, and recommendations -independently. Methods: To achieve our goal we performed -following the best norms in our discipline -a systematic literature review (SLR). We first identified, through searches aptly performed, 422 potentially relevant papers, from which we selected -after applying inclusion and exclusion criteria -30 papers, which we eventually included in our final log. Results: Systems with textual data set preprocess information in nearly the same way and the majority use similarity scores to create recommendations. Systems with GQM-based algorithms consist of questionnaires and require users to explicitly answer questions to produce suggestions. With respect to the recommendations of reviewed systems, they range from application programming interfaces (APIs) to requirements, but no system presently recommends metrics. Conclusion: In our SLR we: (a) identified a sequence of the most popular steps for preprocessing in recommender systems; (b) proposed an optimisation strategy for such steps; (c) found out that the most promising approach includes both ranking and classification; and (d) established that there are no recommendation systems developed to date to process metrics.

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Section: A Preliminary Clusteringmentioning

confidence: 99%

Section: B Rq2: Which Algorithms Underline Recommender Systems For So...mentioning

confidence: 99%

Technologies for GQM-Based Metrics Recommender Systems: A Systematic Literature Review

et al. 2022

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“…Second, Feature and Feature Relation Extraction (FFRE) tool for Eclipse plugin are also introduced to assist the feature model extraction process from the SRS document. This tool use NLP processing to identify actor, action, and object from each requirement sentence and heuristic processing afterward to determine which features are mandatory and which are optional [18]. The third research was done by collaborative researchers between academic and industry practitioners.…”

Section: Related Workmentioning

confidence: 99%

“…Currently, most studies on SPL feature extraction from Software Requirement Specification (SRS) document were processing the list of requirements that have been previously prepared, not SRS as a whole document [17] [18] [19]. As such, this method still needs expert intervention to manually separate requirement sentences from the SRS document that might be tedious and error-prone.…”

Section: Introductionmentioning

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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“…Recommender systems reduce the complexity of comprehension tasks and help to get insights for making decisions [26]. Recommendation techniques have been studied by the SPL research community to support several tasks beyond configuration [13,17]. In this paper, we focus on recommendations to guide the configuration process and we acknowledge several works in this field.…”

Section: Related Workmentioning

confidence: 99%

A Context-Aware Recommender System for Extended Software Product Line Configurations

Pereira

Schulze

Thüm

et al. 2018

Proceedings of the 12th International Workshop on Variability Modelling of Software-Intensive Systems

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Mass customization of standardized products has become a trend to succeed in today's market environment. Software Product Lines (SPLs) address this trend by describing a family of software products that share a common set of features. However, choosing the appropriate set of features that matches a user's individual interests is hampered due to the overwhelming amount of possible SPL configurations. Recommender systems can address this challenge by filtering the number of configurations and suggesting a suitable set of features for the user's requirements. In this paper, we propose a context-aware recommender system for predicting feature selections in an extended SPL configuration scenario, i.e. taking nonfunctional properties of features into consideration. We present an empirical evaluation based on a large real-world dataset of configurations derived from industrial experience in the Enterprise Resource Planning domain. Our results indicate significant improvements in the predictive accuracy of our context-aware recommendation approach over a state-of-the-art binary-based approach.

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Recommending Features and Feature Relationships from Requirements Documents for Software Product Lines

Cited by 17 publications

References 11 publications

Technologies for GQM-Based Metrics Recommender Systems: A Systematic Literature Review

Technologies for GQM-Based Metrics Recommender Systems: A Systematic Literature Review

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

A Context-Aware Recommender System for Extended Software Product Line Configurations

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