IEEE Software
Papers 5082
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#1Rungroj MaipraditH-Index: 1
#2Hideaki HataH-Index: 7
Last.Ken-ichi Matsumoto (Nara Institute of Science and Technology)H-Index: 26
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We propose a sentiment classification method with a general machine learning framework. For feature representation, n-gram IDF is used to extract software-engineering-related, dataset-specific, positive, neutral, and negative n-gram expressions. For classifiers, an automated machine learning tool is used. In the comparison using publicly available datasets, our method achieved the highest F1 values in positive and negative sentences on all datasets.
#1Jeffrey C. Carver (UA: University of Alabama)H-Index: 25
#2Leandro L. Minku (University of Birmingham)H-Index: 18
This issue's "Practitioners' Digest" department reports on the 2019 International Conference on Software Engineering (ICSE). We focus on two emerging themes: security and energy issues for mobile apps. Feedback or suggestions are welcome. In addition, if you try or adopt any of the practices included in this article, please send me and the author(s) of the article a note about your experiences.
Source code reveals abstractions from two places: the problem and the solution. It's easier to design and evolve a system when you understand each of them separately before you combine them in code. With skill, it's possible to separate those concerns in the code. Declarative understanding of the abstractions is the most useful and easy to convey. However, current software development processes rarely guide developers to do this.
Recent studies show that gender diversity in IT teams has a positive impact on the software development process. However, there is still a great gender inequality. The aim of our study was to examine how the working atmosphere depends on the gender differentiation of IT teams. The analysis of the results of the interviews and questionnaires showed that the atmosphere in gender-differentiated teams is more pleasant compared to purely male ones. The paper also discusses the problem of gender discr...
#1Daniel Martens (UHH: University of Hamburg)H-Index: 1
#2Walid Maalej (UHH: University of Hamburg)H-Index: 21
App stores are highly competitive markets, and unexpected app changes might incite even loyal users to explore alternative apps. In this article, we present five release lessons, from emotional patterns identified using sentiment analysis tools, to assist app vendors maintain positive emotions and gain competitive advantages.