Leveraging Official Content and Social Context to Recommend Software Documentation

Li, Jing; Xing, Zhenchang; Kabir, Muhammad Ashad

doi:10.1109/tsc.2018.2812729

Cited by 20 publications

(11 citation statements)

References 57 publications

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“…Recent research from Wu et al (2018) shows that there is a demand to improve the search efficiency of developers by optimizing information enhancement and management, as well as data organization. Li et al (2018) present CnCxL2R, a recommender API documentation that uses the information of the official documentation of APIs and the posts of Stack Overflow to return a ranked list of API documentation.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

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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“…In work [3], a content-driven missing data prediction method is suggested. Typically, such a kind of contentbased prediction is generally dependent on the contents that people have browsed, read or rated in the past.…”

Section: A Missing Data Prediction and Similar Item Clusteringmentioning

confidence: 99%

A Novel Similar Player Clustering Method With Privacy Preservation for Sport Performance Evaluation in Cloud

Xing³

et al. 2021

IEEE Access

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With the ever-increasing popularity of sports and health ideas, people are paying more attentions to gaining high-quality healthy life through various taking various sport items or exercises. Through observing and analyzing the past sport exercise score records, we can cluster the players into different categories, each of which share the same or similar sport preferences or performances. However, the sport exercise score records are often massive and often stored in different cloud platforms, which raise a big difficulty for time-efficient player clustering. Furthermore, the sport exercise score records are a kind of privacy for most players; therefore, it is often not rational or legal to release these sensitive data to the public for similar player clustering purpose. Considering the above two issues, we use SimHash, a kind of privacy-aware approximate neighbor search technique, for similar player clustering by analyzing the sport exercise score records distributed across different cloud platforms. Thus, we can realize privacy-aware similar player clustering through SimHash. At last, we provide a set of experiments to validate the advantages of our proposed privacy-aware similar player clustering algorithm. Reported experimental results show the effectiveness of our proposal in remedying the big data volume and privacy concerns in player clustering based on sport exercise score records.

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“…Jing Li et al [ 24 ] conduct exploratory and hypothesis validation studies that indicate that the presence of hyperlinks in Stack Overflow posts have the potential to aggregate information for developers in a number of other web resources such as official APIs, tutorials, code examples and forum discussions. Jing and Sun Li et al [ 25 , 26 ] discuss the challenges and strategies for facilitating and promoting answers to developers’ questions in software documentation through the content of Stack Overflow posts. They use the context in SO posts to identify SO question-documentation pairs incorporating the content of software documentation and social context on Stack Overflow into a learning-to-rank schema.…”

Section: Related Workmentioning

confidence: 99%

Knowledge reuse in software projects: Retrieving software development Q&A posts based on project task similarity

et al. 2020

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Software developers need to cope with a massive amount of knowledge throughout the typical life cycle of modern projects. This knowledge includes expertise related to the software development phases (e.g., programming, testing) using a wide variety of methods and tools, including development methodologies (e.g., waterfall, agile), software tools (e.g., Eclipse), programming languages (e.g., Java, SQL), and deployment strategies (e.g., Docker, Jenkins). However, there is no explicit integration of these various types of knowledge with software development projects so that developers can avoid having to search over and over for similar and recurrent solutions to tasks and reuse this knowledge. Specifically, Q&A sites such as Stack Overflow are used by developers to share software development knowledge through posts published in several categories, but there is no link between these posts and the tasks developers perform. In this paper, we present an approach that (i) allows developers to associate project tasks with Stack Overflow posts, and (ii) recommends which Stack Overflow posts might be reused based on task similarity. We analyze an industry dataset, which contains project tasks associated with Stack Overflow posts, looking for the similarity of project tasks that reuse a Stack Overflow post. The approach indicates that when a software developer is performing a task, and this task is similar to another task that has been associated with a post, the same post can be recommended to the developer and possibly reused. We believe that this approach can significantly advance the state of the art of software knowledge reuse by supporting novel knowledge-project associations.

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Leveraging Official Content and Social Context to Recommend Software Documentation

Cited by 20 publications

References 57 publications

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

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

A Novel Similar Player Clustering Method With Privacy Preservation for Sport Performance Evaluation in Cloud

Knowledge reuse in software projects: Retrieving software development Q&A posts based on project task similarity

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