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Javascript Quality Assurance Tools and Usage Outcomes in Github Projects

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Javascript Quality Assurance Tools and Usage Outcomes in Github Projects Tool Choice Matters: JavaScript Quality Assurance Tools and Usage Outcomes in GitHub Projects David Kavaler Asher Trockman Bogdan Vasilescu Vladimir Filkov University of California, Davis University of Evansville Carnegie Mellon University University of California, Davis [email protected] [email protected] [email protected] fi[email protected] Abstract—Quality assurance automation is essential in modern TABLE I software development. In practice, this automation is supported TOOL ADOPTION SUMMARY STATISTICS by a multitude of tools that fit different needs and require developers to make decisions about which tool to choose in a # Adoption Events given context. Data and analytics of the pros and cons can inform Across Projects these decisions. Yet, in most cases, there is a dearth of empirical Tool Task class Per tool Per task class evidence on the effectiveness of existing practices and tool choices. david 20; 763 Dependency We propose a general methodology to model the time- bithound 900 23; 917 Management dependent effect of automation tool choice on four outcomes gemnasium 3; 093 of interest: prevalence of issues, code churn, number of pull requests, and number of contributors, all with a multitude of codecov 2; 785 codeclimate Coverage 2; 328 15; 249 controls. On a large data set of npm JavaScript projects, we coveralls 11; 221 extract the adoption events for popular tools in three task classes: linters, dependency managers, and coverage reporters. ESLint 7; 095 Using mixed methods approaches, we study the reasons for the JSHint Linter 2; 876 12; 886 adoptions and compare the adoption effects within each class, and standardJS 3; 435 sequential tool adoptions across classes. We find that some tools Note: 54; 440 total projects under study within each group are associated with more beneficial outcomes 38; 948 projects which adopt tools under study than others, providing an empirical perspective for the benefits 2; 283 projects use different tools in the same task class of each. We also find that the order in which some tools are implemented is associated with varying outcomes. from one tool to another which accomplishes a similar task. I. INTRODUCTION Having multiple tools available for the same task increases The persistent refinement and commoditization of software the complexity of choosing one and integrating it successfully development tools is making development automation possible and beneficially in a project’s pipeline. What’s more, projects and practically available to all. This democratization is in full may have individual, context-specific needs. A problem facing swing: e.g., many GitHub projects run full DevOps continuous maintainers, then, is: given a task, or a series of tasks in a integration (CI) and continuous deployment (CD) pipelines. pipeline, which tools to choose to implement that pipeline? What makes all that possible is the public availability of Organizational science, particularly contingency theory [1], high-quality tools for any important development task (all predicts that no one solution will fit all [2], [3]. Unsurprisingly, usually free for open-source projects). Linters, dependency general advice on which tools are “best” is rare and industrial managers, integration and release tools, etc., are typically well consultants are therefore busy offering DevOps customiza- supported with documentation and are ready to be put to use. tions [4]. At the same time, trace data has been accumulating Often, there are multiple viable tools for any task. E.g., the from open-source projects, e.g., on GitHub, on the choices linters ESLint and standardJS both perform static analysis projects have been making while implementing and evolving and style guideline checking to identify potential issues with their CI pipelines. The comparative data analytics perspective, written code. This presents developers with welcome options, then, implores us to consider an alternative approach: given but also with choices about which of the tools to use. sufficiently large data sets of CI pipeline traces, can we con- Moreover, a viable automation pipeline typically contains textualize and quantify the benefit of incorporating a particular more than one tool, strung together in some order of task tool in a pipeline? accomplishment, each tool performing one or more of the tasks Here we answer this question using a data-driven, at some stage in the process. And the pipelines evolve with comparative, contextual approach to customization problems the projects – adding and removing components is part-and- in development automation pipelines. Starting from a large parcel of modern software development. As shown in Table I, dataset of npm JavaScript packages on GitHub, we mined 38; 948 out of 54; 440 projects gathered in our study use at adoptions of three classes of commonly-used automated least one tool from our set of interest; 12; 109 adopt more quality assurance tools: linters, dependency managers, and than one tool over their lifetime, and 2; 283 projects switch code coverage tools. Within each class, we explore multiple tools and distill fundamental differences in their designs configuration options available to them [10]. Misconfigurations and implementations, e.g., some linters may be designed for have been called “the most urgent but thorny problems in soft- configurability, while others are meant to be used as-is. ware reliability” [11]. In addition, configuration requirements Using this data, and informed by qualitative analyses can change over time as older setups become obsolete, or must (Sec. II-B) of issue and pull request (PR) discussions on be changed due to, e.g., dependency upgrades [12]. GitHub about adopting the different tools, we developed sta- The integration cost for off-the-shelf tools has been long- tistical methods to detect differential effects of using separate studied in computer science [13]–[17]. However, much of this tools within each class on four software maintenance out- research is dated prior to the advent of CI and large social- comes: prevalence of issues (as a quality assurance measure), coding platforms like GitHub. Thus, we believe the topic of code churn (as an indication of setup overhead), number of pull tool integration deserves to be revisited in that setting. requests (for dependency managers, as an indication of tool B. Tool Options Within Three Quality Assurance Task Classes usage overhead), and number of contributors (as an indication of the attractiveness of the project to developers and the ease There are often multiples of quality assurance tools to with which they can adapt to project practices). We then accomplish the same task, and developers constantly evaluate evaluated how different tools accomplishing the same tasks and choose between them. E.g., a web search for JavaScript compare, and if the order in which the tools are implemented linters turns up many blog posts discussing options and pros is associated with changes in outcomes. We found that: and cons [18], [19]. We consider sets of tools that offer similar task functionality as belonging to the same task class of • Projects often switch tools within the same task category, indicating a need for change or that the first tool was not tools. Specifically, we investigate three task classes commonly- the best for their pipeline. studied in prior work (e.g., [20]–[22]) and commonly-used in the JavaScript community: linters, dependency managers, and • Only some tools within the same category seem to be generally beneficial for most projects that use them. code coverage tools. Below, to better illustrate fundamental differences in tools • standardJS, coveralls, and david stand out as tools associated with post-adoption issue prevalence benefits. within the same task class, and note tradeoffs when choosing between tools, we intersperse the description of the tools with • The order in which tools are adopted matters. links to relevant GitHub discussions. To identify these discus- II. JAVASCRIPT CONTINUOUS INTEGRATION PIPELINES sions, we searched for keyword combinations (e.g., “eslint” + WITH DIFFERENT QUALITY ASSURANCE TOOLS “jshint”) on GitHub using the GitHub API (more details in In this work, we study how specific quality assurance Section IV), identifying 47 relevant issue discussion threads tool usage, tool choice, and the tasks they accomplish are that explicitly discuss combinations of our tools of interest. associated with changes in outcomes of importance to software One of the authors then reviewed all matches, removed false engineers, in the context of CI. We focus on CI pipelines positives, and coded all the discussions; the emerging themes 1 used by JavaScript packages published on npm: JavaScript were then discussed and refined by two of the authors. is the most popular language on GitHub and npm is the Linters. Linters are static analysis tools, commonly used to most popular online registry for JavaScript packages, with enforce a common project-wide coding style, e.g., ending lines over 500,000 published packages. While there is hardly any with semicolons. They are also used to detect simple potential empirical evidence supporting the tool choices, there has been bugs, e.g., not handling errors in callback functions. There has much research on CI pipelines. We briefly review related prior been interest in the application of such static analysis tools in work and discuss tool options for our task classes of interest. open-source projects [23] and CI pipelines [24]. Within npm, common linters include ESLint, JSHint, and standardJS. A. CI, Tool Configuration, and Tool Integration Qualitative analysis of 32 issue threads discussing linters CI is a widely-used practice [5], by which all work is suggests that project maintainers and contributors are con- “continuously” compiled, built, and tested; multiple CI tools cerned with specific features (16 discussions) and ease of exist [6]. The effect of CI adoption on measures of project installation (15), with some recommending specific linters success has been studied, with prior work finding beneficial based on personal preferences or popularity, favoring those overall effects [7].
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