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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 285))

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.

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References

  1. Al-Qutaish, R. E. (2010). Quality models in software engineering literature: an analytical and comparative study. Journal of American Science, 6(3), 166–175.

    Google Scholar 

  2. Blei, D. M. (2012). Probabilistic topic models. Commun. ACM, 55(4), 77–84. doi:10.1145/2133806.2133826

  3. Blei, D. M. D., Ng, A. Y. A., & Jordan, M. I. (2003). Latent dirichlet allocation. J. Mach. Learn. Res., 3, 993–1022. Retrieved from http://dl.acm.org/citation.cfm?id=944937

    Google Scholar 

  4. Deerwester, S., & Dumais, S. (1990). Indexing by latent semantic analysis. Journal of the American society for information science, 41(6), 391–407.

    Google Scholar 

  5. Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1–38. Retrieved from http://www.jstor.org/stable/10.2307/2984875

  6. Dromey, R. G. (1995). A model for software product quality. Software Engineering, IEEE Transactions on, 21(2), 146–162. doi:10.1109/32.345830

  7. Garvin, D. A. (1984). What does product quality really mean. Sloan management review, 26(1), 25–43.

    Google Scholar 

  8. Ku, L., Liang, Y., & Chen, H. (2006). Opinion extraction, summarization and tracking in news and blog corpora. In Proceedings of AAAI-2006 Spring Symposium on Computational Approaches to Analyzing Weblogs.

    Google Scholar 

  9. Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259–284. doi:10.1080/01638539809545028

    Google Scholar 

  10. Leopairote, W., Surarerks, A., & Prompoon, N. (2012). Software quality in use characteristic mining from customer reviews. In Digital Information and Communication Technology and it’s Applications (DICTAP), 2012 Second International Conference on (pp. 434–439). Ieee. doi:10.1109/DICTAP.2012.6215397

  11. McCall, J. A., Richards, P. K., & Walters, G. F. (1977). Factors in software quality. General Electric, National Technical Information Service.

    Google Scholar 

  12. Mukherjee, A., & Liu, B. (2012). aspect extraction through Semi-Supervised modeling. Proceedings of 50th anunal meeting of association for computational Linguistics (acL-2012), (July), 339–348.

    Google Scholar 

  13. Qiu, G., Liu, B., Bu, J., & Chen, C. (2009). Expanding domain sentiment lexicon through double propagation. In Proceedings of the 21st international jont conference on Artifical intelligence (pp. 1199–1204).

    Google Scholar 

  14. Qiu, G., Liu, B., Bu, J., & Chen, C. (2011). Opinion word expansion and target extraction through double propagation. Computational linguistics, 37(1), 9–27.

    Google Scholar 

  15. Samadhiya, D., Wang, S.-H., & Chen, D. (2010). Quality models: Role and value in software engineering. In Software Technology and Engineering (ICSTE), 2010 2nd International Conference on (Vol. 1, pp. V1–320 –V1–324). doi:10.1109/ICSTE.2010.5608852

  16. Wong, T.-L., Lam, W., & Wong, T.-S. (2008). An unsupervised framework for extracting and normalizing product attributes from multiple web sites. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 35–42). New York, NY, USA: ACM. doi:10.1145/1390334.1390343

  17. Zhai, Z., Liu, B., Wang, J., Xu, H., & Jia, P. (2012). Product Feature Grouping for Opinion Mining. Intelligent Systems, IEEE, 27(4), 37–44. doi:10.1109/MIS.2011.38

  18. Zhang, L, & Liu, B. (2011). Identifying noun product features that imply opinions. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers (Vol. 2, pp. 575–580).

    Google Scholar 

  19. Zhang, Lei, Liu, B., Lim, S. S. H., & O’Brien-Strain, E. (2010). Extracting and ranking product features in opinion documents. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (pp. 1462–1470). Stroudsburg, PA, USA: Association for Computational Linguistics. Retrieved from http://dl.acm.org/citation.cfm?id=1944566.1944733

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Acknowledgments

This study was supported in part by Universiti Malaysia Sarawak’ Zamalah Graduate Scholarship and grant from ERGS/ICT07(01)/1018/2013(15).

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Correspondence to Issa Atoum .

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© 2014 Springer Science+Business Media Singapore

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Atoum, I., Bong, C.H. (2014). A Framework to Predict Software “Quality in Use” from Software Reviews. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_48

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  • DOI: https://doi.org/10.1007/978-981-4585-18-7_48

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