arXiv subjects

Vladimir Kovalenko

Publications and source records attributed to Vladimir Kovalenko.

At least 19 recordsLinked to original sources

arXiv2025

The Software Infrastructure Attitude Scale (SIAS): A Questionnaire Instrument for Measuring Professionals' Attitudes Toward Technical and Sociotechnical Infrastructure

Context: Recent software engineering (SE) research has highlighted the need for sociotechnical research, implying a demand for customized psychometric scales. Objective: We define the concepts of technical and sociotechnical infrastructure in software engineering, and develop and validate a psychometric scale that measures attitudes toward them. Method: Grounded in theories of infrastructure, attitudes, and prior work on psychometric measurement, we defined the target constructs and generated scale items. The scale was administered to 225 software professionals and evaluated using a split sample. We conducted an exploratory factor analysis (EFA) on one half of the sample to uncover the underlying factor structure and performed a confirmatory factor analysis (CFA) on the other half to validate the structure. Further analyses with the whole sample assessed face, criterion-related, and discriminant validity. Results: EFA supported a two-factor structure (technical and sociotechnical infrastructure), accounting for 65% of the total variance with strong loadings. CFA confirmed excellent model fit. Face and content validity were supported by the item content reflecting cognitive, affective, and behavioral components. Both subscales were correlated with job satisfaction, perceived autonomy, and feedback from the job itself, supporting convergent validity. Regression analysis supported criterion-related validity, while the Heterotrait-Monotrait ratio of correlations (HTMT), the Fornell-Larcker criterion, and model comparison all supported discriminant validity. Discussion: The resulting scale is a valid instrument for measuring attitudes toward technical and sociotechnical infrastructure in software engineering research. Our work contributes to ongoing efforts to integrate psychological measurement rigor into empirical and behavioral software engineering research.

cs.SE↗

arXiv2025

Staying or Leaving? How Job Satisfaction, Embeddedness and Antecedents Predict Turnover Intentions of Software Professionals

Context: Voluntary turnover is common in the software industry, increasing recruitment and onboarding costs and the risk of losing organizational and tacit knowledge. Objective: This study investigates how job satisfaction, work-life balance, job embeddedness, and their antecedents, including job quality, personality traits, attitudes toward technical and sociotechnical infrastructure, and perceptions of organizational justice, relate to software professionals' turnover intentions. Method: We conducted a geographically diverse cross-sectional survey of software professionals (N = 224) and analyzed the data using partial least squares structural equation modeling (PLS-SEM). Our model includes both reflective and formative constructs and tests 15 hypotheses grounded in occupational psychology and software engineering literature. Results: Job satisfaction and embeddedness were significantly negatively associated with software professionals' turnover intentions, while work-life balance showed no direct effect. The strongest antecedents for job satisfaction were work-life balance and job quality, while organizational justice was the strongest predictor of job embeddedness. Discussion: The resulting PLS-SEM model has considerably higher explanatory power for key outcome variables than previous work conducted in the software development context, highlighting the importance of both psychological (e.g., job satisfaction, job embeddedness) and organizational (e.g., organizational justice, job quality) factors in understanding turnover intentions of software professionals. Our results imply that improving job satisfaction and job embeddedness is the key to retaining software professionals. In turn, enhancing job quality, supporting work-life balance, and ensuring high organizational justice can improve job satisfaction and embeddedness, indirectly reducing turnover intentions.

cs.SE↗

arXiv2025

Challenge on Optimization of Context Collection for Code Completion

The rapid advancement of workflows and methods for software engineering using AI emphasizes the need for a systematic evaluation and analysis of their ability to leverage information from entire projects, particularly in large code bases. In this challenge on optimization of context collection for code completion, organized by JetBrains in collaboration with Mistral AI as part of the ASE 2025 conference, participants developed efficient mechanisms for collecting context from source code repositories to improve fill-in-the-middle code completions for Python and Kotlin. We constructed a large dataset of real-world code in these two programming languages using permissively licensed open-source projects. The submissions were evaluated based on their ability to maximize completion quality for multiple state-of-the-art neural models using the chrF metric. During the public phase of the competition, nineteen teams submitted solutions to the Python track and eight teams submitted solutions to the Kotlin track. In the private phase, six teams competed, of which five submitted papers to the workshop.

cs.SE↗

arXiv2024

What Could Possibly Go Wrong: Undesirable Patterns in Collective Development

Software development, often perceived as a technical endeavor, is fundamentally a social activity requiring collaboration among team members. Acknowledging this, the software development community has devised strategies to address possible collaboration-related shortcomings. Various studies have attempted to capture the social dynamics within software engineering. In these studies, the authors developed methods to identify numerous teamwork issues and proposed various approaches to address them. However, certain teamwork issues remain unstudied, necessitating a comprehensive bottom-up exploration from practitioner's perceptions to common patterns. This paper introduces the concept of undesirable patterns in collective development, referring to potential teamwork problems that may escalate if unaddressed. Through 38 in-depth exploratory interviews, we identify and classify 42 patterns, revealing their origins and consequences. Subsequent surveys, 436 and 968 participants each, explore the significance and frequency of the undesirable patterns, and evaluate potential tools and features to manage these patterns. The study contributes a nuanced understanding of undesirable patterns, evaluating their impact and proposing pragmatic tools and features for industrial application. The findings provide a valuable foundation for further in-depth studies and the development of tools to enhance collaborative software engineering practices.

cs.SE↗

arXiv2024

RefExpo: Unveiling Software Project Structures through Advanced Dependency Graph Extraction

The dependency graph (DG) of a software project offers valuable insights for identifying its key components and has been leveraged in numerous studies. However, there is a lack of reusable tools for DG extraction. Existing tools are either outdated and difficult to configure or fail to provide accurate analysis. This study introduces RefExpo, a reusable DG extraction tool that supports multiple languages such as Java, Python, and JavaScript. RefExpo is a plugin based on IntelliJ, a well-maintained and reputed IDE. We also compile an initial version of our dataset, consisting of 20 Java and Python projects. RefExpo's validity is evaluated at two levels: specific language features and comparisons against other tools, referred to as micro and macro levels. Our results show RefExpo achieves 92\% and 100\% recall on micro test suites Judge and PyCG for Python and Java, respectively. In macro-level experiments, RefExpo outperformed existing tools by 31\% and 7\% in finding unique and shared results. The installable version of RefExpo is available on the IntelliJ marketplace, and a short video describing its functionality is available on YouTube.

cs.SE↗

arXiv2024

Bus Factor Explorer

Bus factor (BF) is a metric that tracks knowledge distribution in a project. It is the minimal number of engineers that have to leave for a project to stall. Despite the fact that there are several algorithms for calculating the bus factor, only a few tools allow easy calculation of bus factor and convenient analysis of results for projects hosted on Git-based providers. We introduce Bus Factor Explorer, a web application that provides an interface and an API to compute, export, and explore the Bus Factor metric via treemap visualization, simulation mode, and chart editor. It supports repositories hosted on GitHub and enables functionality to search repositories in the interface and process many repositories at the same time. Our tool allows users to identify the files and subsystems at risk of stalling in the event of developer turnover by analyzing the VCS history. The application and its source code are publicly available on GitHub at https://github.com/JetBrains-Research/bus-factor-explorer. The demonstration video can be found on YouTube: https://youtu.be/uIoV79N14z8

cs.SE↗

arXiv2024

TestSpark: IntelliJ IDEA's Ultimate Test Generation Companion

Writing software tests is laborious and time-consuming. To address this, prior studies introduced various automated test-generation techniques. A well-explored research direction in this field is unit test generation, wherein artificial intelligence (AI) techniques create tests for a method/class under test. While many of these techniques have primarily found applications in a research context, existing tools (e.g., EvoSuite, Randoop, and AthenaTest) are not user-friendly and are tailored to a single technique. This paper introduces TestSpark, a plugin for IntelliJ IDEA that enables users to generate unit tests with only a few clicks directly within their Integrated Development Environment (IDE). Furthermore, TestSpark also allows users to easily modify and run each generated test and integrate them into the project workflow. TestSpark leverages the advances of search-based test generation tools, and it introduces a technique to generate unit tests using Large Language Models (LLMs) by creating a feedback cycle between the IDE and the LLM. Since TestSpark is an open-source (https://github.com/JetBrains-Research/TestSpark), extendable, and well-documented tool, it is possible to add new test generation methods into the plugin with the minimum effort. This paper also explains our future studies related to TestSpark and our preliminary results. Demo video: https://youtu.be/0F4PrxWfiXo

cs.SE↗

arXiv2023

Evolution and fluctuations of chiral chemical potential in heavy ion collisions

The possible appearance of the effects of local parity breaking in the QCD medium formed in heavy ion collisions due to violation of chiral symmetry can be quantified by corresponding chiral chemical potential $\mu_5$. The experimental observables sensitive to the effects of local parity violation in strong interaction include search for polarisation splitting of the $\rho_0$ and $\omega_0$ mesons via angular dependence of spectral functions in their decay to leptons. In this paper we study the space-time evolution and fluctuations of $\mu_5$ using relativistic hydrodynamics and estimate their effect on the light vector meson polarization splitting in Pb-Pb collisions at LHC energy.

hep-ph↗

arXiv2023

Assessing the Impact of File Ordering Strategies on Code Review Process

Popular modern code review tools (e.g. Gerrit and GitHub) sort files in a code review in alphabetical order. A prior study (on open-source projects) shows that the changed files' positions in the code review affect the review process. Their results show that files placed lower in the order have less chance of receiving reviewing efforts than the other files. Hence, there is a higher chance of missing defects in these files. This paper explores the impact of file order in the code review of the well-known industrial project IntelliJ IDEA. First, we verify the results of the prior study on a big proprietary software project. Then, we explore an alternative to the default Alphabetical order: ordering changed files according to their code diff. Our results confirm the observations of the previous study. We discover that reviewers leave more comments on the files shown higher in the code review. Moreover, these results show that, even with the data skewed toward Alphabetical order, ordering changed files according to their code diff performs better than standard Alphabetical order regarding placing problematic files, which needs more reviewing effort, in the code review. These results confirm that exploring various ordering strategies for code review needs more exploration.

cs.SE↗

arXiv2023

Constructing Temporal Networks of OSS Programming Language Ecosystems

One of the primary factors that encourage developers to contribute to open source software (OSS) projects is the collaborative nature of OSS development. However, the collaborative structure of these communities largely remains unclear, partly due to the enormous scale of data to be gathered, processed, and analyzed. In this work, we utilize the World Of Code dataset, which contains commit activity data for millions of OSS projects, to build collaboration networks for ten popular programming language ecosystems, containing in total over 290M commits across over 18M projects. We build a collaboration graph representation for each language ecosystem, having authors and projects as nodes, which enables various forms of social network analysis on the scale of language ecosystems. Moreover, we capture the information on the ecosystems' evolution by slicing each network into 30 historical snapshots. Additionally, we calculate multiple collaboration metrics that characterize the ecosystems' states. We make the resulting dataset publicly available, including the constructed graphs and the pipeline enabling the analysis of more ecosystems.

cs.SE↗

arXiv2022

Bus Factor In Practice

Bus factor is a metric that identifies how resilient is the project to the sudden engineer turnover. It states the minimal number of engineers that have to be hit by a bus for a project to be stalled. Even though the metric is often discussed in the community, few studies consider its general relevance. Moreover, the existing tools for bus factor estimation focus solely on the data from version control systems, even though there exists other channels for knowledge generation and distribution. With a survey of 269 engineers, we find that the bus factor is perceived as an important problem in collective development, and determine the highest impact channels of knowledge generation and distribution in software development teams. We also propose a multimodal bus factor estimation algorithm that uses data on code reviews and meetings together with the VCS data. We test the algorithm on 13 projects developed at JetBrains and compared its results to the results of the state-of-the-art tool by Avelino et al. against the ground truth collected in a survey of the engineers working on these projects. Our algorithm is slightly better in terms of both predicting the bus factor as well as key developers compared to the results of Avelino et al. Finally, we use the interviews and the surveys to derive a set of best practices to address the bus factor issue and proposals for the possible bus factor assessment tool.

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arXiv2021

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

In software engineering, a great number of new approaches are being actively researched, and a lot of tools are being developed based on them. These tools require a framework for their creation and an opportunity to be used by potential developers. Modern IDEs provide both. In this paper, we describe the main capabilities of the IntelliJ Platform that could be useful for researchers that are developing code analysis tools. To illustrate the benefits of using the platform, we describe several use cases that researchers might be interested in: mining software data, running machine learning models on code, recommending refactorings, and visualizing data in the IDE. We provide several examples of existing plugins that implement these cases. Finally, to make it easier to start working with the platform, we develop and provide simple plugins for each use case that could serve as a template for a new project.

cs.SE↗

arXiv2021

RefactorInsight: Enhancing IDE Representation of Changes in Git with Refactorings Information

Inspection of code changes is a time-consuming task that constitutes a big part of everyday work of software engineers. Existing IDEs provide little information about the semantics of code changes within the file editor view. Therefore developers have to track changes across multiple files, which is a hard task with large codebases. In this paper, we present RefactorInsight, a plugin for IntelliJ IDEA that introduces a smart diff for code changes in Java and Kotlin where refactorings are auto-folded and provided with their description, thus allowing users to focus on changes that modify the code behavior like bug fixes and new features. RefactorInsight supports three usage scenarios: viewing smart diffs with auto-folded refactorings and hints, inspecting refactorings in pull requests and in any specific commit in the project change history, and exploring the refactoring history of methods and classes. The evaluation shows that commit processing time is acceptable: on median it is less than 0.2 seconds, which delay does not disrupt developers' IDE workflows. RefactorInsight is available at https://github.com/JetBrains-Research/RefactorInsight. The demonstration video is available at https://youtu.be/-6L2AKQ66nA.

cs.SE↗

arXiv2021

Authorship Attribution of Source Code: A Language-Agnostic Approach and Applicability in Software Engineering

Authorship attribution (i.e., determining who is the author of a piece of source code) is an established research topic. State-of-the-art results for the authorship attribution problem look promising for the software engineering field, where they could be applied to detect plagiarized code and prevent legal issues. With this article, we first introduce a new language-agnostic approach to authorship attribution of source code. Then, we discuss limitations of existing synthetic datasets for authorship attribution, and propose a data collection approach that delivers datasets that better reflect aspects important for potential practical use in software engineering. Finally, we demonstrate that high accuracy of authorship attribution models on existing datasets drastically drops when they are evaluated on more realistic data. We outline next steps for the design and evaluation of authorship attribution models that could bring the research efforts closer to practical use for software engineering.

cs.SE↗

arXiv2021

PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code

The application of machine learning algorithms to source code has grown in the past years. Since these algorithms are quite sensitive to input data, it is not surprising that researchers experiment with input representations. Nowadays, a popular starting point to represent code is abstract syntax trees (ASTs). Abstract syntax trees have been used for a long time in various software engineering domains, and in particular in IDEs. The API of modern IDEs allows to manipulate and traverse ASTs, resolve references between code elements, etc. Such algorithms can enrich ASTs with new data and therefore may be useful in ML-based code analysis. In this work, we present PSIMiner - a tool for processing PSI trees from the IntelliJ Platform. PSI trees contain code syntax trees as well as functions to work with them, and therefore can be used to enrich code representation using static analysis algorithms of modern IDEs. To showcase this idea, we use our tool to infer types of identifiers in Java ASTs and extend the code2seq model for the method name prediction problem.

cs.SE↗

arXiv2021

TNM: A Tool for Mining of Socio-Technical Data from Git Repositories

Networks of collaboration between engineers are reflected in traces of developers' activity in version control systems (VCSs). Extracting data from Git repositories is an essential task for researchers and practitioners working on socio-technical analysis, but it requires substantial engineering work. With increasing interest in analysing socio-technical data and applying it in practice, there are no flexible and easily reusable tools to retrieve socio-technical information from VCSs. With no common reusable toolkit existing for this task, the burden of mining diverts the focus of researchers from their core research questions. In this paper, we present TNM -- an open-source tool for mining socio-technical data from Git repositories. TNM is fast, flexible, and easily extensible. TNM is available on GitHub: https://github.com/JetBrains-Research/tnm

cs.SE↗

arXiv2020

TaskTracker-tool: a Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks

The process of writing code and use of features in an integrated development environment (IDE) is a fruitful source of data in computing education research. Existing studies use records of students' actions in the IDE, consecutive code snapshots, compilation events, and others, to gain deep insight into the process of student programming. In this paper, we present a set of tools for collecting and processing data of student activity during problem-solving. The first tool is a plugin for IntelliJ-based IDEs (PyCharm, IntelliJ IDEA, CLion). By capturing snapshots of code and IDE interaction data, it allows to analyze the process of writing code in different languages -- Python, Java, Kotlin, and C++. The second tool is designed for the post-processing of data collected by the plugin and is capable of basic analysis and visualization. To validate and showcase the toolkit, we present a dataset collected by our tools. It consists of records of activity and IDE interaction events during solution of programming tasks by 148 participants of different ages and levels of programming experience. We propose several directions for further exploration of the dataset.

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arXiv2020

Vector mesons spectrum in a medium with a chiral imbalance induced by the vacuum of fermions

The properties of the light vector mesons in the presence of local parity breaking medium with a chiral imbalance are considered in the vector-meson dominance model. Applying the finite lowest-order radiatively induced local effective Lagrangian, initially developed for the QED, for the vector $ρ$ and $ω$ mesons, we obtained the mass spectrum as a function of momentum and chiral chemical potential $μ_5$. We showed that in addition to the Chern-Simons term, splitting the transverse polarisations of the mesons, there is another radiatively induced contribution that becomes important at momentum and $μ_5$ around a few hundred MeV.

hep-ph↗