Mining Software Quality from Software Reviews: Research Trends and Open Issues
📝 Abstract
Software review text fragments have considerably valuable information about users experience. It includes a huge set of properties including the software quality. Opinion mining or sentiment analysis is concerned with analyzing textual user judgments. The application of sentiment analysis on software reviews can find a quantitative value that represents software quality. Although many software quality methods are proposed they are considered difficult to customize and many of them are limited. This article investigates the application of opinion mining as an approach to extract software quality properties. We found that the major issues of software reviews mining using sentiment analysis are due to software lifecycle and the diverse users and teams.
💡 Analysis
Software review text fragments have considerably valuable information about users experience. It includes a huge set of properties including the software quality. Opinion mining or sentiment analysis is concerned with analyzing textual user judgments. The application of sentiment analysis on software reviews can find a quantitative value that represents software quality. Although many software quality methods are proposed they are considered difficult to customize and many of them are limited. This article investigates the application of opinion mining as an approach to extract software quality properties. We found that the major issues of software reviews mining using sentiment analysis are due to software lifecycle and the diverse users and teams.
📄 Content
Citation Issa Atoum, Ahmed Otoom “Mining Software Quality from Software Reviews: Research Trends and Open Issues”. International Journal of Computer Trends and Technology (IJCTT) V31(2):74-83, January 2016. ISSN:2231-2803. www.ijcttjournal.org . Published by Seventh Sense Research Group.
@article{Atoum2016, author = {Atoum, Issa and Otoom, Ahmed}, doi = {10.14445/22312803/IJCTT-V31P114}, journal = {International Journal of Computer Trends and Technology (IJCTT)}, keywords = {Clustering,Opinion Mining Tasks,Software Quality-in-use,Topic Models}, number = {2}, pages = {74–83}, title = {{Mining Software Quality from Software Reviews : Research Trends and Open Issues}}, url = {http://www.ijcttjournal.org/2016/Volume31/number-2/IJCTT-V31P114.pdf}, volume = {31}, year = {2016} }
Mining Software Quality from Software Reviews: Research Trends and Open Issues Issa Atoum*1, Ahmed Otoom2
1 Faculty of Computer Information, The World Islamic Sciences & Education University, 11947 Amman, Jordan 2 Royal Jordanian Air forces,11134 Amman, Jordan Issa.Atoum@wise.edu.jo aotoom@rjaf.mil.jo
Abstract—Software review text fragments have considerably valuable information about users’ experience. It includes a huge set of properties including the software quality. Opinion mining or sentiment analysis is concerned with analyzing textual user judgments. The application of sentiment analysis on software reviews can find a quantitative value that represents software quality. Although many software quality methods are proposed they are considered difficult to customize and many of them are limited. This article investigates the application of opinion mining as an approach to extract software quality properties. We found that the major issues of software reviews mining using sentiment analysis are due to software lifecycle and the diverse users and teams. Keywords—Software Quality-in-use, Clustering, Topic Models, Opinion Mining Tasks I. INTRODUCTION The World Wide Web and the social media are an invaluable source of business information. For instance, the software reviews on a website can help users make purchase decisions and enable enterprises to improve their business strategies. Studies showed that online reviews have real economic values [1].The process of extracting information for a decision making from text is referred to as opinion mining or sentiment analysis. Formally, “Sentiment analysis or opinion mining refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials” [2, p. 415]. Pang [3] stated that: although many authors use the term “sentiment analysis” to refer to classifying reviews as positive or negative, nowadays it has been taken to mean the computational treatment of opinion, sentiment, and subjectivity in text [3]. Liu [4] identified that the sentiment analysis is more widely used in industry but sentiment analysis and opinion mining are both used in the academia [4]. Both terms are used interchangeably in this article. Thus, opinion mining is important to organizations and individuals. Organizations can study the products (software) trends over time and respond accordingly. On the other hand, software users often seek advices on software products by reading user reviews found on websites such cnet.com, epinions.com and amazon.com. The software reviews are helpful for users in that it has information about user experience (i.e. Software quality). Garvin [5] identified five views/approaches of quality. The nearest definition to this work is the user based approach definition “meeting customer needs”. To our knowledge little research has been published in the domain of opinion mining over software reviews [6], [7][8], [9]. Mining software reviews can save users time and can help them in software selection process that is time consuming. The most widely used surveys[2], [3], [10] are for products in general and none of them have studied the specialty of a specific review domain. The significance of this article is that it is showed by examples and it details the applicability of sentiment analysis tasks over software quality properties. Further this article identifies major issues to software quality mining using sentiment analysis. II. RELATED WORK Software quality has been studied in many models[11], [12] but [13] found that they are limited. Atoum et al. [13] have studied several issues with current software quality models. They showed that studied models are either limited or hard to customize. Atoum et al. [14] suggested to build a dataset of software quality-in-use toward solving this problem. They further proposed two frameworks towards solving this problem[6], [15]. A complete model of software prediction were also proposed in [7], [16]. Their frameworks are based on softwar
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