Extracting features from online software reviews to aid requirements reuse

Bakar, Noor Hasrina; Kasirun, Zarinah Mohd; Salleh, Norsaremah; Jalab, Hamid A.

doi:10.1016/j.asoc.2016.07.048

Cited by 51 publications

(50 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Villarroel et al introduced CLAP to prioritize the clusters of reviews for supporting released planning of apps . Bakara et al proposed a semiautomated approach FENL to extract features for initiating the requirements reuse process . Summarizing the information aims at helping developers understand the main information from large number of reviews, a number of tools are given to achieve this goal, such as WisCom and SUR‐Miner .…”

Section: Related Workmentioning

confidence: 99%

“…13 Bakara et al proposed a semiautomated approach FENL to extract features for initiating the requirements reuse process. 25 Summarizing the information aims at helping developers understand the main information from large number of reviews, a number of tools are given to achieve this goal, such as WisCom 26 and SUR-Miner. 27 These research works can help developers analyze reviews efficiently to gain valuable information, but none of them establishes relationships between such information and domain knowledge so they could only achieve limited effect in supporting the domain analysis of apps.…”

Section: Review Analysismentioning

confidence: 99%

See 1 more Smart Citation

App store mining for iterative domain analysis: Combine app descriptions with user reviews

Liu

et al. 2019

Softw Pract Exp

View full text Add to dashboard Cite

Summary Compared with traditional software, the domain analysis of apps is conducted not only in the early stage of software development to gain knowledge of a particular domain but also runs throughout each iteration of apps to help developers understand evolution trends of the domain for maintaining their competitiveness. In this paper, we propose an approach to analyze app descriptions combined with reviews in App stores automatically and construct a feature‐based domain state model (FDSM) in the form of state machine to support the domain analysis of apps. In FDSM, the domain knowledge up to a certain moment together is defined as a state. Initial state summarizes the high‐level knowledge by gaining topics of app descriptions, whereas each transition is generated based on the information gained within one period of time and describes the change from the current state to the next one. Furthermore, user opinions in reviews are introduced into the model to quantify the value of information for helping developers get key domain knowledge efficiently. To validate the proposed approach, we conducted a series of experiments based on Google Play. The results show that FDSM can provide valuable information for supporting domain analysis, especially in the evolution process of apps.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Review Analysismentioning

confidence: 99%

App store mining for iterative domain analysis: Combine app descriptions with user reviews

Liu

et al. 2019

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…The authors proposed incorporating customer preference information into feature models based on sentiment analysis of user-generated online product reviews [10]. A semi-automated approach was proposed to extract phrases that can represent software features from software reviews as a way to initiate the requirement reuse process [11].…”

Section: Requirements Evolution Prediction Through Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis Based Requirement Evolution Prediction

Zhao

2019

Future Internet

View full text Add to dashboard Cite

To facilitate product developers capturing the varying requirements from users to support their feature evolution process, requirements evolution prediction from massive review texts is in fact of great importance. The proposed framework combines a supervised deep learning neural network with an unsupervised hierarchical topic model to analyze user reviews automatically for product feature requirements evolution prediction. The approach is to discover hierarchical product feature requirements from the hierarchical topic model and to identify their sentiment by the Long Short-term Memory (LSTM) with word embedding, which not only models hierarchical product requirement features from general to specific, but also identifies sentiment orientation to better correspond to the different hierarchies of product features. The evaluation and experimental results show that the proposed approach is effective and feasible.

show abstract

“…Table (a) shows the results by analyzing reviews from 60 Apps in the domain “Social” by LDA. It can be seen that most of words in T0 (Message), T1 (Friend), and T6 (Video) are related to App features, for example, there are only 2 words “day” and “month” in top 10 words of T0 (Message) irrelative to App features; while the situation is opposite in other topics T2 (Ad) to T5 (App), for instance, only 1 word “Photo” in top 10 words of T5 (App) is related to App features, but others are about the sentiments on the product. Other methods try to extract App features from reviews before summarizing them for gaining information that is more related to the features . However, as App features may be expressed by the users with different vocabularies in reviews, it is difficult to extract them accurately: The precisions of these methods are usually about 60% or even lower.…”

Section: Introductionmentioning

confidence: 99%

“…• Other methods try to extract App features from reviews before summarizing them for gaining information that is more related to the features. [15][16][17] However, as App features may be expressed by the users with different vocabularies in reviews, it is difficult to extract them accurately: The precisions of these methods are usually about 60% or even lower.…”

mentioning

confidence: 99%

Analyzing reviews guided by App descriptions for the software development and evolution

Liu

et al. 2018

J Software Evolu Process

View full text Add to dashboard Cite

Reviews in App stores are a massive and fast‐growing data resource for developers to understand user experiences and their needs. Studies show that users often express their sentiments on App features in reviews, and this information is important for the development and evolution of Apps. To help developers gain such information efficiently, this paper proposes a method using App descriptions, another typical data in App stores, to guide the analysis of reviews. Firstly, we extract App features from descriptions, then summarize them to gain topics of App features as high‐level information; the results are formalized as a topic‐based domain model (TBDM). Secondly, we train classifiers of reviews based on the model to establish the relationships between user sentiments and App features. Finally, a quantified method is given to analyze the model based on developer preferences for recommending and summarizing reviews. To evaluate our approach, experiments were conducted using the App descriptions and reviews collected from Google Play. The results indicate that the approach can classify reviews to their related App features effectively (average F measure is 86.13%), and provides useful information for overall analyzing App features in a domain and identifying (dis)advantages of an App.

show abstract

Extracting features from online software reviews to aid requirements reuse

Cited by 51 publications

References 14 publications

App store mining for iterative domain analysis: Combine app descriptions with user reviews

App store mining for iterative domain analysis: Combine app descriptions with user reviews

Sentiment Analysis Based Requirement Evolution Prediction

Analyzing reviews guided by App descriptions for the software development and evolution

Contact Info

Product

Resources

About