A benchmark study on sentiment analysis for software engineering research

Novielli, Nicole; Girardi, Daniela; Lanubile, Filippo

doi:10.1145/3196398.3196403

Cited by 88 publications

(96 citation statements)

References 38 publications

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“…For instance, the Dissensus smell arises when developers are not able to reach a consensus with respect to the patch to be applied: likely, in this case the conversations among developers will report contrasting opinions that can be identified using opinion mining techniques or contrasting sentiment detectable using sentiment analysis [141]. In this regard, it is worth remarking that recent findings on sentiment analysis [142], [143] revealed that existing tools are not always suitable for software engineering purposes, thus suggesting that the ability of detecting community smells may depend on the advances of other research fields. At the same time, Code Red-which is the smell arising when only 1-2 maintainers can refactor the source code-may be structurally identifiable looking at the developers' collaboration network and applying heuristics to discriminate how many developers over the history of a class applied refactoring operations on it.…”

Section: Resultsmentioning

confidence: 99%

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Palomba

Tamburri

Fontana

et al. 2021

IIEEE Trans. Software Eng.

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Section: Resultsmentioning

confidence: 99%

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Palomba

Tamburri

Fontana

et al. 2021

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

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“…Although we involve both high-level attributes and keywords, some other characteristics such as review title length and post date, which would be helpful for response generation, are not considered. Besides, the review sentiment predicted by SentiStrength [40] might not be reliable [61], and could influence the generated response. However, accurate sentiment prediction based on reviews is out of the scope of this paper, and the effectiveness of StentiStrength in detecting user sentiment about app features has been demonstrated in [39].…”

Section: Threats To Validitymentioning

confidence: 99%

Automating App Review Response Generation

Gao

Zeng

Xia

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times compared to not responding. To alleviate the labor burden in replying to the bulk of user reviews, developers usually adopt a template-based strategy where the templates can express appreciation for using the app or mention the company email address for users to follow up. However, reading a large number of user reviews every day is not an easy task for developers. Thus, there is a need for more automation to help developers respond to user reviews.Addressing the aforementioned need, in this work we propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. RRGen explicitly incorporates review attributes, such as user rating and review length, and learns the relations between reviews and corresponding responses in a supervised way from the available training data. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4 (an accuracy measure that is widely used to evaluate dialogue response generation systems). Qualitative analysis also confirms the effectiveness of RRGen in generating relevant and accurate responses.

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“…us, one possible way of addressing and performing the sentiment analysis would be adopting the work of [80,81] for Q&A posts. Nevertheless, another challenging issue that needs to be addressed is investigating the suitability of adopting different sentiment analysis techniques as reported in [82][83][84][85] for our domain.…”

Section: Need For Sentiment Analysismentioning

confidence: 99%

A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow

Ahmad

Feng

Khan

et al. 2020

Security and Communication Networks

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Context. The improvements made in the last couple of decades in the requirements engineering (RE) processes and methods have witnessed a rapid rise in effectively using diverse machine learning (ML) techniques to resolve several multifaceted RE issues. One such challenging issue is the effective identification and classification of the software requirements on Stack Overflow (SO) for building quality systems. The appropriateness of ML-based techniques to tackle this issue has revealed quite substantial results, much effective than those produced by the usual available natural language processing (NLP) techniques. Nonetheless, a complete, systematic, and detailed comprehension of these ML based techniques is considerably scarce. Objective. To identify or recognize and classify the kinds of ML algorithms used for software requirements identification primarily on SO. Method. This paper reports a systematic literature review (SLR) collecting empirical evidence published up to May 2020. Results. This SLR study found 2,484 published papers related to RE and SO. The data extraction process of the SLR showed that (1) Latent Dirichlet Allocation (LDA) topic modeling is among the widely used ML algorithm in the selected studies and (2) precision and recall are amongst the most commonly utilized evaluation methods for measuring the performance of these ML algorithms. Conclusion. Our SLR study revealed that while ML algorithms have phenomenal capabilities of identifying the software requirements on SO, they still are confronted with various open problems/issues that will eventually limit their practical applications and performances. Our SLR study calls for the need of close collaboration venture between the RE and ML communities/researchers to handle the open issues confronted in the development of some real world machine learning-based quality systems.

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A benchmark study on sentiment analysis for software engineering research

Cited by 88 publications

References 38 publications

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?

Automating App Review Response Generation

A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow

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