What are Software Engineers asking about Android Testing on Stack Overflow?

Villanes, Isabel K.; Ascate, Silvia M.; Gomes, Josias; Dias-Neto, Arilo Cláudio

doi:10.1145/3131151.3131157

Cited by 19 publications

(8 citation statements)

References 22 publications

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“…There are studies on specific research technologies such as mobile development (Linares-Vásquez et al, 2013;Fontão et al, 2018), libraries of IOS and Android (W. Wang & Godfrey, 2013), security in development (Yang et al, 2016), Android testing (Villanes et al, 2017), concurrent programming (Ahmed & Bagherzadeh, 2018), and front-end technologies in web development (Bajaj, 2012). In these studies, the dataset is filtered.…”

Section: Related Workmentioning

confidence: 99%

“…Yang et al (2016) used an approach consisting of the average approved response time, the average number of answers and the average number of views. Villanes et al (2017) filtered questions containing "what," "why," and "how" in their title. Ahmed & Bagherzadeh (2018) developed an approach from the percentage of questions with no accepted answers and the time taken for the approved answer to the questions.…”

Section: Related Workmentioning

confidence: 99%

“…They compared the number of posts from libraries with the number of calls from libraries in projects by type. Villanes et al (2017) used the number of the questions and answers belonging to test tools and test libraries to show trends in test tools and strategies. Ahmed & Bagherzadeh (2018) used a p number to analyze the correlation between popular and difficult topics.…”

Section: Related Workmentioning

confidence: 99%

“…As a contribution to LDA, Yang et al (2016) tuned LDA with a genetic algorithm. A silhouette coefficient was used to detect the optimum topic number (Fontão et al, 2018 ;Villanes et al, 2017). Zou et al (2015) received support from four Ph.D. students to verify extracted topics manually.…”

Section: Related Workmentioning

confidence: 99%

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Java Geliştiricileri Stack Overflow Üzerinde Ne Hakkında Konuşuyor? Deneysel Bir Çalışma

Şahin¹,

Bayazit²

2020

European Journal of Science and Technology

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Java has been a widely used programming language for a long time in various fields. Java and its libraries have been frequently updated for various reasons including bugs, change requests, performance and usability requirements and so on. In this paper, we examine how these changes affect the use of Java and analyze trends in its usage. As a data source, we use the Stack Overflow public dataset which is the largest online Q&A site about software technologies. We firstly employ a practical approach to extract the Javarelated posts from the Stack Overflow dataset using cosine similarity and compare it with previous works. We then apply Latent Dirichlet Allocation (LDA) to the corpus for topic modelling. We divided the data set into two-year periods to obtain consistent clusters. After obtaining main topics, we examine topics and keywords on a two-year basis. Finally, unlikely previous works, we manually classify topics into two as "domain-specific" and "development environment" and investigate tendencies of these classes to change in both the short term and the long term.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Java Geliştiricileri Stack Overflow Üzerinde Ne Hakkında Konuşuyor? Deneysel Bir Çalışma

Şahin¹,

Bayazit²

2020

European Journal of Science and Technology

View full text Add to dashboard Cite

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“…Villanes et al [107] performed a study using the Stack Overflow 16 with the intention to analyze and cluster the main topics of interest on Android testing. One of their results pointed out that recently, developers have shown increased interest in the Appium 17 , Espresso, Monkey, and Robotium tools.…”

Section: Rq 12: What Are the Most Used Ones?mentioning

confidence: 99%

Testing tools for Android context-aware applications: a systematic mapping

2019

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Context: Mobile devices, such as smartphones, have increased their capacity of information processing and sensors have been aggregated to their hardware. Such sensors allow capturing information from the environment in which they are introduced. As a result, mobile applications that use the environment and user information to provide services or perform context-based actions are increasingly common. This type of application is known as context-aware application. While software testing is an expensive activity in general, testing context-aware applications is an even more expensive and challenging activity. Thus, efforts are needed to automate testing for context-aware applications, particularly in the scope of Android, which is currently the most used operating system by smartphones. Objective: This paper aims to identify and discuss the state-of-the-art tools that allow the automation of testing Android context-aware applications. Method: In order to do so, we carried out a systematic mapping study (SMS) to find out the studies in the existing literature that describe or present Android testing tools. The discovered tools were then analyzed to identify their potential in testing Android context-aware applications. Result: A total of 68 works and 80 tools were obtained as a result of the SMS. From the identified tools, five are context-aware Android application testing tools, and five are general Android application testing tools, but support the test of the context-aware feature. Conclusion: Although context-aware application testing tools do exist, they do not support automatic generation or execution of test cases focusing on high-level contexts. Moreover, they do not support asynchronous context variations.

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What kind of questions do developers ask on Stack Overflow? A comparison of automated approaches to classify posts into question categories

et al. 2019

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On question and answer sites, such as Stack Overflow (SO), developers use tags to label the content of a post and to support developers in question searching and browsing. However, these tags mainly refer to technological aspects instead of the purpose of the question. Tagging questions with their purpose can add a new dimension to the identification of discussed topics in posts on SO. In this paper, we aim at automating the classification of SO question posts into seven question categories. As a first step, we harmonized existing taxonomies of question categories and then, we manually classified 1,000 SO questions according to our new taxonomy. Additionally to the question category, we marked the phrases that indicate a question category for each of the posts. We then use this data set to automate the classification of posts using two approaches. For the first approach, we manually analyzed the phrases to find patterns. Based on regular expressions, we implemented a classifier, for each of the categories, that determines whether a post belongs to a category. These regular expressions are derived by analyzing patterns in the phrases. In the second approach, we use the curated data set to train classification models of supervised machine learning algorithms (Random Forest and Support Vector Machines). For the machine learning algorithms, we experimented with 1,312 different configurations regarding the preprocessing of the text and the representation of the input data. Then, we compared the performance of the regex approach with the performance of the best configuration that uses machine learning algorithms on a validation set of 110 posts. The results show that using the regular expression approach, we can classify posts into the correct question category with an average precision and recall of 0.90, and an MCC of 0.68. Additionally, we applied the regex approach on all questions of SO that deal with Android app development and investigated the co-occurrence of question categories in posts. We found that the categories API USAGE, CONCEPTUAL, and DISCREPANCY are the most frequently assigned question categories and that they also occur together frequently. Our approach can be used to support developers in browsing SO discussions or researchers in building recommender systems based on SO.

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What are Software Engineers asking about Android Testing on Stack Overflow?

Cited by 19 publications

References 22 publications

Java Geliştiricileri Stack Overflow Üzerinde Ne Hakkında Konuşuyor? Deneysel Bir Çalışma

Java Geliştiricileri Stack Overflow Üzerinde Ne Hakkında Konuşuyor? Deneysel Bir Çalışma

Testing tools for Android context-aware applications: a systematic mapping

What kind of questions do developers ask on Stack Overflow? A comparison of automated approaches to classify posts into question categories

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