On-demand feature recommendations derived from mining public product descriptions

Dumitru, Horatiu; Gibiec, Marek; Hariri, Negar; Cleland‐Huang, Jane; Mobasher, Bamshad; Castro-Herrera, Carlos; Mirakhorli, Mehdi

doi:10.1145/1985793.1985819

Cited by 124 publications

(115 citation statements)

References 25 publications

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“…Few approaches have been proposed to extract variability from informal product descriptions [50,7]. Dumitru et al [50] implemented a recommender system that models and recommends product features for a given domain.…”

Section: Related Workmentioning

confidence: 99%

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Automated extraction of product comparison matrices from informal product descriptions

Nasr

Bécan

Acher

et al. 2017

Journal of Systems and Software

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Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product.In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM -despite the informality and absence of structure in the textual descriptions of products.We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users.

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Section: Related Workmentioning

confidence: 99%

“…Dumitru et al [50] implemented a recommender system that models and recommends product features for a given domain.…”

Section: Related Workmentioning

confidence: 99%

Automated extraction of product comparison matrices from informal product descriptions

Nasr

Bécan

Acher

et al. 2017

Journal of Systems and Software

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, there is no existing work that proposes requirements traceability using information retrieval that is based on a domain metamodel and its instances. Recently, Dumitru et al [19] or Tung et al [20] mined Softpedia products information to propose enhanced recommending systems. These approaches mined static contents that do not require further synchronization accordingly to a more changing and dynamic model.…”

Section: Related Workmentioning

confidence: 99%

INCREMENT: A Mixed MDE-IR Approach for Regulatory Requirements Modeling and Analysis

Sannier

Baudry

2014

Requirements Engineering: Foundation for Software Quality

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“…Since many stakeholders are engaged in maintaining software, these stakeholders benefit from properly categorized software repositories for two reasons. First, grouping applications with similar features allows stakeholders to decide what features they should implement in their own applications that belong to same groups or categories (Kawaguchi et al 2006;Dumitru et al 2011). Second, stakeholders can determine what problems or bugs are common to many applications in the same category, and in turn predict what problems or bugs other applications from the same category are likely to encounter (Weiss et al 2007;Zimmermann et al 2009); this type of prediction could be used as a quality assurance technique to recognize typical bad smells or mistakes in the code that should be avoided during programming.…”

Section: Introductionmentioning

confidence: 99%

On using machine learning to automatically classify software applications into domain categories

et al. 2012

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Software repositories hold applications that are often categorized to improve the effectiveness of various maintenance tasks. Properly categorized applications allow stakeholders to identify requirements related to their applications and predict maintenance problems in software projects. Manual categorization is expensive, tedious, and laborious -this is why automatic categorization approaches are gaining widespread importance. Unfortunately, for different legal and organizational reasons, the applications' source code is often not available, thus making it difficult to automatically categorize these applications. In this paper, we propose a novel approach in which we use Application Programming Interface (API) calls from third-party libraries for automatic categorization of software applications that use these API calls. Our approach is general since it enables different categorization algorithms to be applied to repositories that contain both source code and bytecode of applications, since API calls can be extracted from both the source code and byte-code. We compare our approach to a state-of-the-art approach that uses machine learning algorithms for software categorization, and conduct experiments on two large Java repositories: an open-source repository containing 3,286 projects and a closed-source repository with 745 applications, where the source code was not available. Our contribution is twofold: we propose a new approach that makes it possible to categorize software projects without any source code using a small number of API calls as attributes, and furthermore we carried out a comprehensive empirical evaluation of automatic categorization approaches.

show abstract

On-demand feature recommendations derived from mining public product descriptions

Cited by 124 publications

References 25 publications

Automated extraction of product comparison matrices from informal product descriptions

Automated extraction of product comparison matrices from informal product descriptions

INCREMENT: A Mixed MDE-IR Approach for Regulatory Requirements Modeling and Analysis

On using machine learning to automatically classify software applications into domain categories

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