JavaScript is one of the web's key building blocks. It is used by the majority of web sites and it is supported by all modern browsers. We present the first large-scale study of client-side JavaScript code over time. Specifically, we have collected and analyzed a dataset containing daily snapshots of JavaScript code coming from Alexa's Top 10000 web sites (~7.5 GB per day) for nine consecutive months, to study different temporal aspects of web client code. We found that scripts change often; typically every few days, indicating a rapid pace in web applications development. We also found that the lifetime of web sites themselves, measured as the time between JavaScript changes, is also short, in the same time scale. We then performed a qualitative analysis to investigate the nature of the changes that take place. We found that apart from standard changes such as the introduction of new functions, many changes are related to online configuration management. In addition, we examined JavaScript code reuse over time and especially the widespread reliance on third-party libraries. Furthermore, we observed how quality issues evolve by employing established static analysis tools to identify potential software bugs, whose evolution we tracked over time. Our results show that quality issues seem to persist over time, while vulnerable libraries tend to decrease.@en

Empirical software engineering work often involves studying revision control system repositories maintained using the popular Git system. Checking out each revision one wants to study is inefficient. On the other hand the examination of directories and files of past revisions using Git's commands suffers from a usability perspective. We introduce RepoFS, a tool that exposes a Git repository as a virtual user-level file system. Commits, branches, and tags appear as separate directory trees allowing them to be efficiently processed through command-line tools and file explorers. We illustrate these points through motivating examples and discuss the advantages and drawbacks of the proposed approach.@en

The ICSE 2021 paper titled 'PyCG: Practical Call Graph Generation in Python' comes with a replication package with the purpose of providing open access to (1) our prototype call graph generator, namely PyCG, and (2) the data and scripts that replicate the results of the paper. The Artifact Evaluation Committee found that this package leads to the reproduction of the results outlined in the paper and is openly available1. The replication package contains the following: 1) A Docker image which can be either built manually or downloaded from DockerHub. It contains the source code and installation of PyCG, as well as the installations of two other call graph generators (i.e., Pyan and Depends), which we compare PyCG with. 2) A micro-benchmark suite of 112 Python modules (Section I-A). 3) A macro-benchmark suite of 5 popular Python packages (Section I-B). 4) Python and Bash scripts used to execute PyCG, Pyan and Depends against the micro- and macro-benchmarks and compare the corresponding results.@en