Holistic recommender systems for software engineering

Ponzanelli, Luca

doi:10.1145/2591062.2591081

Cited by 12 publications

(3 citation statements)

References 80 publications

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“…Following Stefik and Siebert's conjecture that syntactical variations of programming language constructs might affect accuracy among programmers [15], PYCEE then looks up each word in an online resource cataloguing syntax across programming languages. 10 PYCEE attempts to locate each word in one of the tables in this resource, and if successful, replaces 9 https://stackoverflow.com/a/12836173 10 http://rigaux.org/language-study/syntax-across-languages.html the word with the most frequently occurring word in the same table while removing any non-letters. Adopting the insights of related work with regard to the importance of tasks (e.g., [16])-programmers are usually not interested in a concept by itself, but work with the concept as part of an action or task-PYCEE then attempts to find actions associated with each word.…”

Section: A Compiler Error Message Parsing and Query Constructionmentioning

confidence: 99%

Enhancing Python Compiler Error Messages via Stack

Thiselton

2019

2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background: Compilers tend to produce cryptic and uninformative error messages, leaving programmers confused and requiring them to spend precious time to resolve the underlying error. To find help, programmers often take to online question-and-answer forums such as Stack Overflow to start discussion threads about the errors they encountered.Aims: We conjecture that information from Stack Overflow threads which discuss compiler errors can be automatically collected and repackaged to provide programmers with enhanced compiler error messages, thus saving programmers' time and energy.Method: We present Pycee, a plugin integrated with the popular Sublime Text IDE to provide enhanced compiler error messages for the Python programming language. Pycee automatically queries Stack Overflow to provide customised and summarised information within the IDE. We evaluated two Pycee variants through a think-aloud user study during which 16 programmers completed Python programming tasks while using Pycee.Results: The majority of participants agreed that Pycee was helpful while completing the study tasks. When compared to a baseline relying on the official Python documentation to enhance compiler error messages, participants generally preferred Pycee in terms of helpfulness, citing concrete suggestions for fixes and example code as major benefits.Conclusions: Our results confirm that data from online sources such as Stack Overflow can be successfully used to automatically enhance compiler error messages. Our work opens up venues for future work to further enhance compiler error messages as well as to automatically reuse content from Stack Overflow for other aspects of programming.

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Section: A Compiler Error Message Parsing and Query Constructionmentioning

confidence: 99%

Enhancing Python Compiler Error Messages via Stack

Thiselton

2019

2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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“…In software engineering domain, the systems SeaHawk [28] and Prompter [29] provide CA-CB recommendations that support programmers to complete issues and bugs by using query completion and recommends StackOverflow questions, in which the context is the specific part of the source code from which the recommendations are requested. In the same domain, Libra [30] integrates recommendations but it also considers, in addition to context extracted from the integrated development environment, resources opened by the user such as URLs or documents to better understand his/her context.…”

Section: Related Work On Qa Recommendationsmentioning

confidence: 99%

A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items

et al. 2019

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Content-Based recommender systems (CB) filter relevant items to users in overloaded search spaces using information about their preferences. However, classical CB scheme is mainly based on matching between items descriptions and user profile, without considering that context may influence user preferences. Therefore, it cannot achieve high accuracy on user preference prediction. This paper aims to handle context-awareness (CA) to improve quality of recommendation taking contextual information as the trend in current trend interest, in which a stream of status updates can be analyzed to model the context. It proposes a novel CA-CB approach that recommends question/answer items by considering context awareness based on topic detection within current trend interest. A case study and related experiments were developed in the big data framework Spark to show that the context integration benefits recommendation performance.INDEX TERMS Content-based recommender system, context-awareness, user profile contextualization, map-reduce, big data.• A suitable way to study status updates, i.e. a userprovided free text, in the QA recommendation scenario to provide recommendations tailored to both the user preferences and current trend interest.

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“…Existing work on recommender systems in software engineering mainly support activities such as domain analysis and requirements elicitation [11], reusing functional code snippets [12], analyzing low level implementations [13]- [15], and other activities such as debugging and writing bug reports [16], [17].…”

Section: Related Workmentioning

confidence: 99%

Mining Big Data for Detecting, Extracting and Recommending Architectural Design Concepts

Mirakhorli

Chen

Kazman

2015

2015 IEEE/ACM 1st International Workshop on Big Data Software Engineering

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An architecture recommender system can help programmers make better design choices to address their architectural quality attribute concerns while doing their daily programming tasks. We mine big data to detect and extract a large set of architectural design concepts, such as design patterns, design tactics, architecture styles, etc., to be used in our architecture recommender system called ARS. However, mining big data poses many practical challenges for system implementation. The volume, velocity and variety of our data set, like all other big data systems, requires careful planning. This first challenge is to select appropriate technologies from the large number of available products for our system implementation. Building on these technologies our greatest challenge is to custom-fit our algorithms to the parallel processing platform we have selected for ARS, to meet our performance goals.

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Holistic recommender systems for software engineering

Cited by 12 publications

References 80 publications

Enhancing Python Compiler Error Messages via Stack

Enhancing Python Compiler Error Messages via Stack

A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items

Mining Big Data for Detecting, Extracting and Recommending Architectural Design Concepts

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