Software quality in use characteristic mining from customer reviews
16 May 2012-pp 434-439
TL;DR: A methodology for software product reviews mining based on software quality ontology constructed from ISO 9126 and a rule-based classification to finally produce software quality in use scores forSoftware product Representation is proposed.
Abstract: Reviews from customers who have experience with the software product are an important information decision making for software product acquisition. They usually appear on ecommerce websites or any online download market. If some products have a large number of reviews, customer may not have time to read all of them. Therefore, we need to extract software information characteristic from reviews in order to provide product review representation. Customer can further use it to compare one software product attributes and other products' attributes. Software product quality from user point of view may be used to characterize each software product. ISO 9126 is widely used among software engineer to assess software quality in use. It covers software quality model and contains the quality model characteristic from user perspective: effectiveness, productivity, safety and satisfaction. We propose a methodology for software product reviews mining based on software quality ontology constructed from ISO 9126 and a rule-based classification to finally produce software quality in use scores for software product Representation. The quality in use score for each software characteristic can be used to preliminary determine the quality of the software.
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TL;DR: The QinUF evaluation over real-life scenarios showed that the QinUF automates software QinU measurement; therefore, users could compare and acquire software on the fly.
21 citations
29 May 2013
TL;DR: In this research, software quality extracted from user perspective with respect to ISO 9126 is selected to be the characteristic model and a methodology for a software product reviews mining based on software quality ontology and a product software quality in use scores for software review representation is proposed.
Abstract: Reviews of software from experienced users play an important role for software acquisition decision. In order to share their experiences, an online software recommendation system has been developed. This information is not only useful for users or customers, but it is also be used for evaluating the software. Since there are many of reviews are accumulated and expressed in both formal and informal written languages, it takes time for concluding the evaluation. Therefore, we are interested in an automatically process to extract software information attributes from the reviews in order to provide software review representation. One essential problem is the different sentiment of the same sentence in different environment. To solve this problem, rule-based classification is used as our machine learning model. In this research, software quality extracted from user perspective with respect to ISO 9126 is selected to be the characteristic model. We also propose a methodology for a software product reviews mining based on software quality ontology and a product software quality in use scores for software review representation. Our classification approach is applied from two lists of sentiment words (positive and negative words) combining with rule-based classification method. Our result yields four percent of the accuracy improvement from simple classification applied only two lists of sentiment words.
17 citations
Cites methods from "Software quality in use characteris..."
...In our previous work [14], we proposed a methodology for software product reviews mining based on software quality ontology and a rule-based classification....
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...In our previous work [14], ontology is identified from ISO 9126 and expanded by WordNet3....
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Posted Content•
TL;DR: In this paper, the authors identify and explain major issues and challenges in measuring software quality in use in the context of the ISO SQuaRE series and related software quality models and highlight open research areas.
Abstract: Software quality in use comprises quality from the user's perspective. It has gained its importance in e-government applications, mobile-based applications, embedded systems, and even business process development. User's decisions on software acquisitions are often ad hoc or based on preference due to difficulty in quantitatively measuring software quality in use. But, why is quality-in-use measurement difficult? Although there are many software quality models, to the authors' knowledge no works survey the challenges related to software quality-in-use measurement. This article has two main contributions: 1) it identifies and explains major issues and challenges in measuring software quality in use in the context of the ISO SQuaRE series and related software quality models and highlights open research areas; and 2) it sheds light on a research direction that can be used to predict software quality in use. In short, the quality-in-use measurement issues are related to the complexity of the current standard models and the limitations and incompleteness of the customized software quality models. A sentiment analysis of software reviews is proposed to deal with these issues.
17 citations
01 Jan 2014
TL;DR: A framework to detect software “quality in use” as defined by the ISO/IEC 25010 standard is presented here and employs opinionfeature double propagation to expand predefined lists of software ‘quality in use’ features to domain specific features.
Abstract: Software reviews are verified to be a good source of users’ experience The software “quality in use” concerns meeting users’ needs Current software quality models such as McCall and Boehm, are built to support software development process, rather than users perspectives In this paper, opinion mining is used to extract and summarize software “quality in use” from software reviews A framework to detect software “quality in use” as defined by the ISO/IEC 25010 standard is presented here The framework employs opinion-feature double propagation to expand predefined lists of software “quality in use” features to domain specific features Clustering is used to learn software feature “quality in use” characteristics group A preliminary result of extracted software features shows promising results in this direction
12 citations
Cites methods from "Software quality in use characteris..."
...A famous topic modeling model is called LSI or LSA[9], [10]....
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Posted Content•
TL;DR: This paper presents major issues and challenges in measuring software quality-in-use in the context of the ISO SQuaRE series and related software quality models, presents a novel framework that can be used to predict software quality in-use, and presents preliminary results of quality-In-use topic prediction.
Abstract: Software quality-in-use comprehends the quality from user’s perspectives. It has gained its importance in e-learning applications, mobile service based applications and project management tools. User’s decisions on software acquisitions are often ad hoc or based on preference due to difficulty in quantitatively measure software quality-in-use. However, why qualityin-use measurement is difficult? Although there are many software quality models to our knowledge, no works surveys the challenges related to software quality-in-use measurement. This paper has two main contributions; 1) presents major issues and challenges in measuring software quality-in-use in the context of the ISO SQuaRE series and related software quality models, 2) Presents a novel framework that can be used to predict software quality-in-use, and 3) presents preliminary results of quality-in-use topic prediction. Concisely, the issues are related to the complexity of the current standard models and the limitations and incompleteness of the customized software quality models. The proposed framework employs sentiment analysis techniques to predict software quality-in-use.
12 citations
Cites background from "Software quality in use characteris..."
...Activity based models usually tracks development or testing activities rather than user activities ....
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References
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TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.
Abstract: Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].
15,068 citations
"Software quality in use characteris..." refers background in this paper
...Sentiment analysis primarily focuses on the classification of reviews into two poles of opposite qualities, e.g. positive and negative....
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...Based on our survey of product reviews, they are almost relatively short and are not formally written....
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Book•
08 Jul 2008TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
Abstract: An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.
This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
7,452 citations
22 Aug 2004
TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Abstract: Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.
7,330 citations
"Software quality in use characteris..." refers background in this paper
...For example, a set of defined ontology for the physical components of digital cameras includes battery, lens, LCD, buttons and flash....
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...A collection of reviews may be useful for 978-1-4673-0734-5/12/$31.00 ©2012 IEEE 434 analyzing and making a decision on product selection according to the special requirements or product quality....
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Posted Content•
TL;DR: A simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (Thumbs down) if the average semantic orientation of its phrases is positive.
Abstract: This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
4,526 citations
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TL;DR: This special section includes descriptions of five recommender systems, which provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients, and which combine evaluations with content analysis.
Abstract: Recommender systems assist and augment this natural social process. In a typical recommender system people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients. In some cases the primary transformation is in the aggregation; in others the system’s value lies in its ability to make good matches between the recommenders and those seeking recommendations. The developers of the first recommender system, Tapestry [1], coined the phrase “collaborative filtering” and several others have adopted it. We prefer the more general term “recommender system” for two reasons. First, recommenders may not explictly collaborate with recipients, who may be unknown to each other. Second, recommendations may suggest particularly interesting items, in addition to indicating those that should be filtered out. This special section includes descriptions of five recommender systems. A sixth article analyzes incentives for provision of recommendations. Figure 1 places the systems in a technical design space defined by five dimensions. First, the contents of an evaluation can be anything from a single bit (recommended or not) to unstructured textual annotations. Second, recommendations may be entered explicitly, but several systems gather implicit evaluations: GroupLens monitors users’ reading times; PHOAKS mines Usenet articles for mentions of URLs; and Siteseer mines personal bookmark lists. Third, recommendations may be anonymous, tagged with the source’s identity, or tagged with a pseudonym. The fourth dimension, and one of the richest areas for exploration, is how to aggregate evaluations. GroupLens, PHOAKS, and Siteseer employ variants on weighted voting. Fab takes that one step further to combine evaluations with content analysis. ReferralWeb combines suggested links between people to form longer referral chains. Finally, the (perhaps aggregated) evaluations may be used in several ways: negative recommendations may be filtered out, the items may be sorted according to numeric evaluations, or evaluations may accompany items in a display. Figures 2 and 3 identify dimensions of the domain space: The kinds of items being recommended and the people among whom evaluations are shared. Consider, first, the domain of items. The sheer volume is an important variable: Detailed textual reviews of restaurants or movies may be practical, but applying the same approach to thousands of daily Netnews messages would not. Ephemeral media such as netnews (most news servers throw away articles after one or two weeks) place a premium on gathering and distributing evaluations quickly, while evaluations for 19th century books can be gathered at a more leisurely pace. The last dimension describes the cost structure of choices people make about the items. Is it very costly to miss IT IS OFTEN NECESSARY TO MAKE CHOICES WITHOUT SUFFICIENT personal experience of the alternatives. In everyday life, we rely on
3,993 citations