Search for: All records

Software developers frequently use the system shell to perform configuration management tasks. Unfortunately, the shell does not scale well to large systems, and configuration management tools like Ansible are more difficult to learn. We address this problem with Dozer, a technique to help developers push their shell commands into Ansible task definitions. It operates by tracing and comparing system calls to find Ansible modules with similar behaviors to shell commands, then generating and validating migrations to find the task which produces the most similar changes to the system. Dozer is syntax agnostic, which should allow it to generalize to other configuration management platforms. We evaluate Dozer using datasets from open source configuration scripts. 

Code snippets are prevalent, but are hard to reuse because they often lack an accompanying environment configuration. Most are not actively maintained, allowing for drift between the most recent possible configuration and the code snippet as the snippet becomes out-of-date over time. Recent work has identified the problem of validating and detecting out-of-date code snippets as the most important consideration for code reuse. However, determining if a snippet is correct, but simply out-of-date, is a non-trivial task. In the best case, breaking changes are well documented, allowing developers to manually determine when a code snippet contains an out-of-date API usage. In the worst case, determining if and when a breaking change was made requires an exhaustive search through previous dependency versions. We present V2, a strategy for determining if a code snippet is out-of-date by detecting discrete instances of configuration drift, where the snippet uses an API which has since undergone a breaking change. Each instance of configuration drift is classified by a failure encountered during validation and a configuration patch, consisting of dependency version changes, which fixes the underlying fault. V2 uses feedback-directed search to explore the possible configuration space for a code snippet, reducing the number of potential environment configurations that need to be validated. When run on a corpus of public Python snippets from prior research, V2 identifies 248 instances of configuration drift. 

Platforms like Stack Overflow and GitHub's gist system promote the sharing of ideas and programming techniques via the distribution of code snippets designed to illustrate particular tasks. Python, a popular and fast-growing programming language, sees heavy use on both sites, with nearly one million questions asked on Stack Overflow and 400 thousand public gists on GitHub. Unfortunately, around 75% of the Python example code shared through these sites cannot be directly executed. When run in a clean environment, over 50% of public Python gists fail due to an import error for a missing library. We present DockerizeMe, a technique for inferring the dependencies needed to execute a Python code snippet without import error. DockerizeMe starts with offline knowledge acquisition of the resources and dependencies for popular Python packages from the Python Package Index (PyPI). It then builds Docker specifications using a graph-based inference procedure. Our inference procedure resolves import errors in 892 out of nearly 3,000 gists from the Gistable dataset for which Gistable's baseline approach could not find and install all dependencies. 

Software developers create and share code online to demonstrate programming language concepts and programming tasks. Code snippets can be a useful way to explain and demonstrate a programming concept, but may not always be directly executable. A code snippet can contain parse errors, or fail to execute if the environment contains unmet dependencies. This paper presents an empirical analysis of the executable status of Python code snippets shared through the GitHub gist system, and the ability of developers familiar with software configuration to correctly configure and run them. We find that 75.6% of gists require non-trivial configuration to overcome missing dependencies, configuration files, reliance on a specific operating system, or some other environment configuration. Our study also suggests the natural assumption developers make about resource names when resolving configuration errors is correct less than half the time. We also present Gistable, a database and extensible framework built on GitHub's gist system, which provides executable code snippets to enable reproducible studies in software engineering. Gistable contains 10,259 code snippets, approximately 5,000 with a Dockerfile to configure and execute them without import error. Gistable is publicly available at https://github.com/gistable/gistable. 

Flaky tests are a source of frustration and uncertainty for developers. In an educational environment, flaky tests can create doubts related to software behavior and student grades, especially when the grades depend on tests passing. NC State University's junior-level software engineering course models industrial practice through team-based development and testing of new features on a large electronic health record (EHR) system, iTrust2. Students are expected to maintain and supplement an extensive suite of UI tests using Selenium WebDriver. Team builds are run on the course's continuous integration (CI) infrastructure. Students report, and we confirm, that tests that pass on one build will inexplicably fail on the next, impacting productivity and confidence in code quality and the CI system. The goal of this work is to find and fix the sources of flaky tests in iTrust2. We analyze configurations of Selenium using different underlying web browsers and timeout strategies (waits) for both test stability and runtime performance. We also consider underlying hardware and operating systems. Our results show that HtmlUnit with Thread waits provides the lowest number of test failures and best runtime on poor-performing hardware. When given more resources (e.g., more memory and a faster CPU), Google Chrome with Angular waits is less flaky and faster than HtmlUnit, especially if the browser instance is not restarted between tests. The outcomes of this research are a more stable and substantially faster teaching application and a recommendation on how to configure Selenium for applications similar to iTrust2 that run in a CI environment.