The IntelliJ Platform: A Framework for Building Plugins and Mining Software Data

Kurbatova, Zarina; Golubev, Yaroslav; Kovalenko, Vladimir; Bryksin, Timofey

doi:10.1109/asew52652.2021.00016

Cited by 15 publications

(11 citation statements)

References 11 publications

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“…In this study, we introduced a new algorithm, called PSI-E, that has been developed based on Equation 4and integrated into the T-SXM tool so that it can interpret and extract ESM values for different types of JREs from software modules. The PSI-E algorithm has been built on top of the PSI (Program Structure Interface) -a layer in IntelliJ Platform [46] which is responsible for parsing files and creating syntactic and semantic code model.…”

Section: B Extract Esm Valuesmentioning

confidence: 99%

Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

2023

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In software development, identifying software faults is an important task. The presence of faults not only reduces the quality of the software, but also increases the cost of development life cycle. Fault identification can be performed by analysing the characteristics of the buggy source codes from the past and predict the present ones based on the same characteristics using statistical or machine learning models. Many studies have been conducted to predict the fault proneness of software systems. However, most of them provide either inadequate or insufficient information and thus make the fault prediction task difficult. In this paper, we present a novel set of software metrics called Error-type software metrics, which provides prediction models with information about patterns of different types of Java runtime error. Particular, in this study, the ESM values consist of information of three common Java runtime errors which are Index Out Of Bounds Exception, Null Pointer Exception, and Class Cast Exception. Also, we proposed a methodology for modelling, extracting, and evaluating error patterns from software modules using Stream X-Machine (a formal modelling method) and machine learning techniques. The experimental results showed that the proposed Error-type software metrics could significantly improve the performances of machine learning models in fault-proneness prediction.

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Section: B Extract Esm Valuesmentioning

confidence: 99%

Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

2023

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“…To do that, the plugin tries to build a PSI tree of the fragment. A PSI (Program Structure Interface) tree is a concrete syntax tree that is used in the IntelliJ Platform to represent the code [54]. If a PSI tree can be built and represents a valid statement, and if the duplicates still remain after the delay, the code fragment is passed to the Code Analyzer.…”

Section: Tool Implementationmentioning

confidence: 99%

Just-in-time code duplicates extraction

AlOmar

Ivanov

Kurbatova³

et al. 2023

Information and Software Technology

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“…IDEs based on the IntelliJ Platform [22] (IntelliJ IDEA, PyCharm, WebStorm, etc. ), developed by JetBrains [20], naturally also have a code completion feature.…”

Section: Introductionmentioning

confidence: 99%

All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logs

Bibaev¹,

Kalina²,

Lomshakov³

et al. 2022

Preprint

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Integrated Development Environments (IDEs) are designed to make users more productive, as well as to make their work more comfortable. To achieve this, a lot of diverse tools are embedded into IDEs, and the developers of IDEs can employ anonymous usage logs to collect the data about how they are being used to improve them. A particularly important component that this can be applied to is code completion, since improving code completion using statistical learning techniques is a well-established research area.In this work, we propose an approach for collecting completion usage logs from the users in an IDE and using them to train a machine learning based model for ranking completion candidates. We developed a set of features that describe completion candidates and their context, and deployed their anonymized collection in the Early Access Program of IntelliJ-based IDEs. We used the logs to collect a dataset of code completions from users, and employed it to train a ranking CatBoost model. Then, we evaluated it in two settings: on a held-out set of the collected completions and in a separate A/B test on two different groups of users in the IDE. Our evaluation shows that using a simple ranking model trained on the past user behavior logs significantly improved code completion experience. Compared to the default heuristics-based ranking, our model demonstrated a decrease in the number of typing actions necessary to perform the completion in the IDE from 2.073 to 1.832.The approach adheres to privacy requirements and legal constraints, since it does not require collecting personal information, performing all the necessary anonymization on the client's side. Importantly, it can be improved continuously: implementing new features, collecting new data, and evaluating new models -this way, we have been using it in production since the end of 2020.

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The IntelliJ Platform: A Framework for Building Plugins and Mining Software Data

Cited by 15 publications

References 11 publications

Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

Just-in-time code duplicates extraction

All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logs

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