“An Empirical Study of Yanked Releases in the Rust Package Registry” accepted in TSE!

Li Hao’s paper “An Empirical Study of Yanked Releases in the Rust Package Registry” was accepted for publication in the Transactions on Software Engineering (TSE) journal! Super congrats Li Hao! This was a collaboration with Filipe R. Cogo from the Huawei Centre of Software Excellence.

“Cargo, the software packaging manager of Rust, provides a yank mechanism to support release-level deprecation, which
can prevent packages from depending on yanked releases. Most prior studies focused on code-level (i.e., deprecated APIs) and
package-level deprecation (i.e., deprecated packages). However, few studies have focused on release-level deprecation. In this study,
we investigate how often and how the yank mechanism is used, the rationales behind its usage, and the adoption of yanked releases in
the Cargo ecosystem. Our study shows that 9.6% of the packages in Cargo have at least one yanked release, and the proportion of
yanked releases kept increasing from 2014 to 2020. Package owners yank releases for other reasons than withdrawing a defective
release, such as fixing a release that does not follow semantic versioning or indicating a package is removed or replaced. In addition,
we found that 46% of the packages directly adopted at least one yanked release and the yanked releases propagated through the
dependency network, which leads to 1.4% of the releases in the ecosystem having unresolved dependencies.”

See our Publications for the full paper.

“Studying the Performance Risks of Upgrading Docker Hub Images: A Case Study of WordPress” accepted at ICPE 2022!

Mikael’s paper “Studying the Performance Risks of Upgrading Docker Hub Images: A Case Study of WordPress” was accepted for publication at ICPE 2022! Super congrats Mikael!

“The Docker Hub repository contains Docker images of applications, which allow users to do in-place upgrades to benefit from the latest released features and security patches. However, prior work showed that upgrading a Docker image not only changes the main application, but can also change many dependencies. In this paper, we present a methodology to study the performance impact of upgrading the Docker Hub image of an application, thereby focusing on changes to dependencies. We demonstrate our methodology through a case study of 90 official images of the WordPress application. Our study shows that Docker image users should be cautious and conduct a performance test before upgrading to a newer Docker image in most cases. Our methodology can assist them to better understand the performance risks of such upgrades, and helps them to decide how thorough such a performance test should be.”

See our Publications for the full paper.

“Using Natural Language Processing Techniques to Improve Manual Test Case Descriptions” accepted at ICSE-SEIP 2022!

Markos’ paper “Using Natural Language Processing Techniques to Improve Manual Test Case Descriptions” was accepted for publication at the Software Engineering in Practice (SEIP) track of ICSE 2022! Super congrats Markos!

“Despite the recent advancements in test automation, software testing often remains a manual, and costly, activity in many industries. Manual test cases, often described only in natural language, consist of one or more test steps, which are instructions that must be performed to achieve the testing objective. Having different employees specifying test cases might result in redundant, unclear, or incomplete test cases. Manually reviewing and validating newly-specified test cases is time-consuming and becomes impractical in a scenario with a large test suite. Therefore, in this paper, we propose an automated framework to automatically analyze test cases that are specified in natural language and provide actionable recommendations on how to improve the test cases. Our framework consists of configurable components and modules for analysis, which are capable of recommending improvements to the following: (1) the terminology of a new test case through language modeling, (2) potentially missing test steps for a new test case through frequent itemset and association rule mining, and (3) recommendation of similar test cases that already exist in the test suite through text embedding and clustering. We thoroughly evaluated the three modules on data from our industry partner. Our framework can provide actionable recommendations, which is an important challenge given the widespread occurrence of test cases that are described only in natural language in the software industry (in particular, the game industry).”

See our Publications for the full paper.