Data Mining Practical Machine Learning Tools and Techniques

物件導向軟體之架構(Object-Oriented Software Construction)探討

การวจยเชงคณภาพ เปนเครองมอสำคญอยางหนงสำหรบทำความเขาใจสงคมและพฤตกรรมมนษย การวจยแบบการสรางทฤษฎจากขอมล กเปนหนงในหลายระเบยบวธการวจยเชงคณภาพทกำลงไดรบความสนใจ และเปนทนยมเพมสงขนเรอยๆ จากนกวชาการ และนกวจยในสาขาสงคมศาสตร และศาสตรอนๆ เชน พฤตกรรมศาสตร สงคมวทยา สาธารณสขศาสตร พยาบาลศาสตร จตวทยาสงคม ศกษาศาสตร รฐศาสตร และสารสนเทศศกษา ดงนน หนงสอเรอง “ConstructingGrounded Theory: A Practical Guide through Qualitative Analysis” หรอ “การสรางทฤษฎจากขอมล:แนวทางการปฏบตผานการวเคราะหเชงคณภาพ” จะชวยใหผอานมความรความเขาใจถงพฒนาการของปฏบตการวจยแบบสรางทฤษฎจากขอมล ตลอดจนแนวทาง และกระบวนการปฏบตการวจยอยางเปนระบบ จงเปนหนงสอทควรคาแกการอานโดยเฉพาะนกวจยรนใหม เพอเปนแนวทางในการนำความรความเขาใจไประยกตในงานวจยของตน อกทงนกวจยผเชยวชาญสามารถอานเพอขยายมโนทศนดานวจยใหกวางขวางขน

Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

Preface 1. The approximation problem and existence of best approximations 2. The uniqueness of best approximations 3. Approximation operators and some approximating functions 4. Polynomial interpolation 5. Divided differences 6. The uniform convergence of polynomial approximations 7. The theory of minimax approximation 8. The exchange algorithm 9. The convergence of the exchange algorithm 10. Rational approximation by the exchange algorithm 11. Least squares approximation 12. Properties of orthogonal polynomials 13. Approximation of periodic functions 14. The theory of best L1 approximation 15. An example of L1 approximation and the discrete case 16. The order of convergence of polynomial approximations 17. The uniform boundedness theorem 18. Interpolation by piecewise polynomials 19. B-splines 20. Convergence properties of spline approximations 21. Knot positions and the calculation of spline approximations 22. The Peano kernel theorem 23. Natural and perfect splines 24. Optimal interpolation Appendices Index.

Approximation theory and methods, by M. J. D. Powell. Pp 339. £25 (hardcover), £8·50 (paperback). 1981. ISBN 0-521-22472-1/29514-9 (Cambridge University Press)

Programming question and answer (QA questions in some topics lead to discussions in other topics; and the topics gaining the most popularity over time are web development (especially jQuery), mobile applications (especially Android), Git, and MySQL.

/pdf/what-are-developers-talking-about-an-analysis-of-topics-and-1uk58lpy70.pdf

What are developers talking about? An analysis of topics and trends in Stack Overflow

App stores allow users to submit feedback for downloaded apps in form of star ratings and text reviews. Recent studies analyzed this feedback and found that it includes information useful for app developers, such as user requirements, ideas for improvements, user sentiments about specific features, and descriptions of experiences with these features. However, for many apps, the amount of reviews is too large to be processed manually and their quality varies largely. The star ratings are given to the whole app and developers do not have a mean to analyze the feedback for the single features. In this paper we propose an automated approach that helps developers filter, aggregate, and analyze user reviews. We use natural language processing techniques to identify fine-grained app features in the reviews. We then extract the user sentiments about the identified features and give them a general score across all reviews. Finally, we use topic modeling techniques to group fine-grained features into more meaningful high-level features. We evaluated our approach with 7 apps from the Apple App Store and Google Play Store and compared its results with a manually, peer-conducted analysis of the reviews. On average, our approach has a precision of 0.59 and a recall of 0.51. The extracted features were coherent and relevant to requirements evolution tasks. Our approach can help app developers to systematically analyze user opinions about single features and filter irrelevant reviews.

/pdf/how-do-users-like-this-feature-a-fine-grained-sentiment-4el59000r2.pdf

How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews

Application distribution platforms - or app stores - such as Google Play or Apple AppStore allow users to submit feedback in form of ratings and reviews to downloaded applications. In the last few years, these platforms have become very popular to both application developers and users. However, their real potential for and impact on requirements engineering processes are not yet well understood. This paper reports on an exploratory study, which analyzes over one million reviews from the Apple AppStore. We investigated how and when users provide feedback, inspected the feedback content, and analyzed its impact on the user community. We found that most of the feedback is provided shortly after new releases, with a quickly decreasing frequency over time. Reviews typically contain multiple topics, such as user experience, bug reports, and feature requests. The quality and constructiveness vary widely, from helpful advices and innovative ideas to insulting offenses. Feedback content has an impact on download numbers: positive messages usually lead to better ratings and vice versa. Negative feedback such as shortcomings is typically destructive and misses context details and user experience. We discuss our findings and their impact on software and requirements engineering teams.

http://mobis.informatik.uni-hamburg.de/wp-content/uploads/2013/07/RE2013PaganoMaalej.pdf

User feedback in the appstore: An empirical study

App stores like Google Play and Apple AppStore have over 3 Million apps covering nearly every kind of software and service. Billions of users regularly download, use, and review these apps. Recent studies have shown that reviews written by the users represent a rich source of information for the app vendors and the developers, as they include information about bugs, ideas for new features, or documentation of released features. This paper introduces several probabilistic techniques to classify app reviews into four types: bug reports, feature requests, user experiences, and ratings. For this we use review metadata such as the star rating and the tense, as well as, text classification, natural language processing, and sentiment analysis techniques. We conducted a series of experiments to compare the accuracy of the techniques and compared them with simple string matching. We found that metadata alone results in a poor classification accuracy. When combined with natural language processing, the classification precision got between 70–95% while the recall between 80–90%. Multiple binary classifiers outperformed single multiclass classifiers. Our results impact the design of review analytics tools which help app vendors, developers, and users to deal with the large amount of reviews, filter critical reviews, and assign them to the appropriate stakeholders.

https://mobis.informatik.uni-hamburg.de/wp-content/uploads/2015/06/review_classification_preprint.pdf

Bug report, feature request, or simply praise? On automatically classifying app reviews

Research in program comprehension has considerably evolved over the past two decades. However, only little is known about how developers practice program comprehension under time and project pressure, and which methods and tools proposed by researchers are used in industry. This paper reports on an observational study of 28 professional developers from seven companies, investigating how developers comprehend software. In particular we focus on the strategies followed, information needed, and tools used. We found that developers put themselves in the role of end users by inspecting user interfaces. They try to avoid program comprehension, and employ recurring, structured comprehension strategies depending on work context. Further, we found that standards and experience facilitate comprehension. Program comprehension was considered a subtask of other maintenance tasks rather than a task by itself. We also found that face-to-face communication is preferred to documentation. Overall, our results show a gap between program comprehension research and practice as we did not observe any use of state of the art comprehension tools and developers seem to be unaware of them. Our findings call for further careful analysis and for reconsidering research agendas.

https://www.computer.org/csdl/pds/api/csdl/proceedings/download-article/12OmNAle6Mv/pdf

How do professional developers comprehend software

Nowadays, users can easily submit feedback about software products in app stores, social media, or user groups. Moreover, software vendors are collecting massive amounts of implicit feedback in the form of usage data, error logs, and sensor data. These trends suggest a shift toward data-driven user-centered identification, prioritization, and management of software requirements. Developers should be able to adopt the requirements of masses of users when deciding what to develop and when to release. They could systematically use explicit and implicit user data in an aggregated form to support requirements decisions. The goal is data-driven requirements engineering by the masses and for the masses.

Walid Maalej

Papers

How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews

User feedback in the appstore: An empirical study

Bug report, feature request, or simply praise? On automatically classifying app reviews

How do professional developers comprehend software

Toward Data-Driven Requirements Engineering