arXiv subjects

Marco Tulio Valente

Publications and source records attributed to Marco Tulio Valente.

51 records · Page 3Linked to original sources

arXiv2017

RefDiff: Detecting Refactorings in Version Histories

Refactoring is a well-known technique that is widely adopted by software engineers to improve the design and enable the evolution of a system. Knowing which refactoring operations were applied in a code change is a valuable information to understand software evolution, adapt software components, merge code changes, and other applications. In this paper, we present RefDiff, an automated approach that identifies refactorings performed between two code revisions in a git repository. RefDiff employs a combination of heuristics based on static analysis and code similarity to detect 13 well-known refactoring types. In an evaluation using an oracle of 448 known refactoring operations, distributed across seven Java projects, our approach achieved precision of 100% and recall of 88%. Moreover, our evaluation suggests that RefDiff has superior precision and recall than existing state-of-the-art approaches.

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arXiv2017

On the Popularity of GitHub Applications: A Preliminary Note

GitHub is the world's largest collection of open source software. Therefore, it is important both to software developers and users to compare and track the popularity of GitHub repositories. In this paper, we propose a framework to assess the popularity of GitHub software, using their number of stars. We also propose a set of popularity growth patterns, which describe the evolution of the number of stars of a system over time. We show that stars tend to correlate with other measures, like forks, and with the effective usage of GitHub software by third-party programs. Throughout the paper we illustrate the application of our framework using real data extracted from GitHub.

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arXiv2017

Assessing Code Authorship: The Case of the Linux Kernel

Code authorship is a key information in large-scale open source systems. Among others, it allows maintainers to assess division of work and identify key collaborators. Interestingly, open-source communities lack guidelines on how to manage authorship. This could be mitigated by setting to build an empirical body of knowledge on how authorship-related measures evolve in successful open-source communities. Towards that direction, we perform a case study on the Linux kernel. Our results show that: (a) only a small portion of developers (26 %) makes significant contributions to the code base; (b) the distribution of the number of files per author is highly skewed --- a small group of top authors (3 %) is responsible for hundreds of files, while most authors (75 %) are responsible for at most 11 files; (c) most authors (62 %) have a specialist profile; (d) authors with a high number of co-authorship connections tend to collaborate with others with less connections.

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arXiv2017

Refactoring Legacy JavaScript Code to Use Classes: The Good, The Bad and The Ugly

JavaScript systems are becoming increasingly complex and large. To tackle the challenges involved in implementing these systems, the language is evolving to include several constructions for programming- in-the-large. For example, although the language is prototype-based, the latest JavaScript standard, named ECMAScript 6 (ES6), provides native support for implementing classes. Even though most modern web browsers support ES6, only a very few applications use the class syntax. In this paper, we analyze the process of migrating structures that emulate classes in legacy JavaScript code to adopt the new syntax for classes introduced by ES6. We apply a set of migration rules on eight legacy JavaScript systems. In our study, we document: (a) cases that are straightforward to migrate (the good parts); (b) cases that require manual and ad-hoc migration (the bad parts); and (c) cases that cannot be migrated due to limitations and restrictions of ES6 (the ugly parts). Six out of eight systems (75%) contain instances of bad and/or ugly cases. We also collect the perceptions of JavaScript developers about migrating their code to use the new syntax for classes.

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arXiv2016

AngularJS in the Wild: A Survey with 460 Developers

To implement modern web applications, a new family of JavaScript frameworks has emerged, using the MVC pattern. Among these frameworks, the most popular one is AngularJS, which is supported by Google. In spite of its popularity, there is not a clear knowledge on how AngularJS design and features affect the development experience of Web applications. Therefore, this paper reports the results of a survey about AngularJS, including answers from 460 developers. Our contributions include the identification of the most appreciated features of AngularJS (e.g., custom interface components, dependency injection, and two-way data binding) and the most problematic aspects of the framework (e.g., performance and implementation of directives).

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arXiv2016

Understanding the Factors that Impact the Popularity of GitHub Repositories

Software popularity is a valuable information to modern open source developers, who constantly want to know if their systems are attracting new users, if new releases are gaining acceptance, or if they are meeting user's expectations. In this paper, we describe a study on the popularity of software systems hosted at GitHub, which is the world's largest collection of open source software. GitHub provides an explicit way for users to manifest their satisfaction with a hosted repository: the stargazers button. In our study, we reveal the main factors that impact the number of stars of GitHub projects, including programming language and application domain. We also study the impact of new features on project popularity. Finally, we identify four main patterns of popularity growth, which are derived after clustering the time series representing the number of stars of 2,279 popular GitHub repositories. We hope our results provide valuable insights to developers and maintainers, which can help them on building and evolving systems in a competitive software market.

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arXiv2016

Predicting the Popularity of GitHub Repositories

GitHub is the largest source code repository in the world. It provides a git-based source code management platform and also many features inspired by social networks. For example, GitHub users can show appreciation to projects by adding stars to them. Therefore, the number of stars of a repository is a direct measure of its popularity. In this paper, we use multiple linear regressions to predict the number of stars of GitHub repositories. These predictions are useful both to repository owners and clients, who usually want to know how their projects are performing in a competitive open source development market. In a large-scale analysis, we show that the proposed models start to provide accurate predictions after being trained with the number of stars received in the last six months. Furthermore, specific models---generated using data from repositories that share the same growth trends---are recommended for repositories with slow growth and/or for repositories with less stars. Finally, we evaluate the ability to predict not the number of stars of a repository but its rank among the GitHub repositories. We found a very strong correlation between predicted and real rankings (Spearman's rho greater than 0.95).

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arXiv2016

Why We Refactor? Confessions of GitHub Contributors

Refactoring is a widespread practice that helps developers to improve the maintainability and readability of their code. However, there is a limited number of studies empirically investigating the actual motivations behind specific refactoring operations applied by developers. To fill this gap, we monitored Java projects hosted on GitHub to detect recently applied refactorings, and asked the developers to ex- plain the reasons behind their decision to refactor the code. By applying thematic analysis on the collected responses, we compiled a catalogue of 44 distinct motivations for 12 well-known refactoring types. We found that refactoring activity is mainly driven by changes in the requirements and much less by code smells. Extract Method is the most versatile refactoring operation serving 11 different purposes. Finally, we found evidence that the IDE used by the developers affects the adoption of automated refactoring tools.

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arXiv2016

Towards a Technique for Extracting Microservices from Monolithic Enterprise Systems

The idea behind microservices architecture is to develop a single large, complex application as a suite of small, cohesive, independent services. On the other way, monolithic systems get larger over the time, deviating from the intended architecture, and becoming risky and expensive to evolve. This paper describes a technique to identify and define microservices on monolithic enterprise systems. As the major contribution, our evaluation shows that our approach was able to identify relevant candidates to become microservices on a 750 KLOC banking system.

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arXiv2016

A Novel Approach for Estimating Truck Factors

Truck Factor (TF) is a metric proposed by the agile community as a tool to identify concentration of knowledge in software development environments. It states the minimal number of developers that have to be hit by a truck (or quit) before a project is incapacitated. In other words, TF helps to measure how prepared is a project to deal with developer turnover. Despite its clear relevance, few studies explore this metric. Altogether there is no consensus about how to calculate it, and no supporting evidence backing estimates for systems in the wild. To mitigate both issues, we propose a novel (and automated) approach for estimating TF-values, which we execute against a corpus of 133 popular project in GitHub. We later survey developers as a means to assess the reliability of our results. Among others, we find that the majority of our target systems (65%) have TF <= 2. Surveying developers from 67 target systems provides confidence towards our estimates; in 84% of the valid answers we collect, developers agree or partially agree that the TF's authors are the main authors of their systems; in 53% we receive a positive or partially positive answer regarding our estimated truck factors.

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arXiv2016

Does Technical Debt Lead to the Rejection of Pull Requests?

Technical Debt is a term used to classify non-optimal solutions during software development. These solutions cause several maintenance problems and hence they should be avoided or at least documented. Although there are a considered number of studies that focus on the identification of Technical Debt, we focus on the identification of Technical Debt in pull requests. Specifically, we conduct an investigation to reveal the different types of Technical Debt that can lead to the rejection of pull requests. From the analysis of 1,722 pull requests, we classify Technical Debt in seven categories namely design, documentation, test, build, project convention, performance, or security debt. Our results indicate that the most common category of Technical Debt is design with 39.34%, followed by test with 23.70% and project convention with 15.64%. We also note that the type of Technical Debt influences on the size of push request discussions, e.g., security and project convention debts instigate more discussion than the other types.

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arXiv2016

JSClassFinder: A Tool to Detect Class-like Structures in JavaScript

With the increasing usage of JavaScript in web applications, there is a great demand to write JavaScript code that is reliable and maintainable. To achieve these goals, classes can be emulated in the current JavaScript standard version. In this paper, we propose a reengineering tool to identify such class-like structures and to create an object-oriented model based on JavaScript source code. The tool has a parser that loads the AST (Abstract Syntax Tree) of a JavaScript application to model its structure. It is also integrated with the Moose platform to provide powerful visualization, e.g., UML diagram and Distribution Maps, and well-known metric values for software analysis. We also provide some examples with real JavaScript applications to evaluate the tool.

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arXiv2015

DCLfix: A Recommendation System for Repairing Architectural Violations

Architectural erosion is a recurrent problem in software evolution. Despite this fact, the process is usually tackled in ad hoc ways, without adequate tool support at the architecture level. To address this shortcoming, this paper presents a recommendation system -- called DCLfix -- that provides refactoring guidelines for maintainers when tackling architectural erosion. In short, DCLfix suggests refactoring recommendations for violations detected after an architecture conformance process using DCL, an architectural constraint language

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arXiv2015

JExtract: An Eclipse Plug-in for Recommending Automated Extract Method Refactorings

Although Extract Method is a key refactoring for improving program comprehension, refactoring tools for such purpose are often underused. To address this shortcoming, we present JExtract, a recommendation system based on structural similarity that identifies Extract Method refactoring opportunities that are directly automated by IDE-based refactoring tools. Our evaluation suggests that JExtract is far more effective (w.r.t. recall and precision) to identify misplaced code in methods than JDeodorant, a state-of-the-art tool

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arXiv2015

ModularityCheck: A Tool for Assessing Modularity using Co-Change Clusters

It is widely accepted that traditional modular structures suffer from the dominant decomposition problem. Therefore, to improve current modularity views, it is important to investigate the impact of design decisions concerning modularity in other dimensions, as the evolutionary view. In this paper, we propose the ModularityCheck tool to assess package modularity using co-change clusters, which are sets of classes that usually changed together in the past. Our tool extracts information from version control platforms and issue reports, retrieves co-change clusters, generates metrics related to co-change clusters, and provides visualizations for assessing modularity. We also provide a case study to evaluate the tool. http://youtu.be/7eBYa2dfIS8

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