Empirical Study of the Docker Smells Impact on the Image Size

Docker, a widely adopted tool for packaging and deploying applications leverages Dockerfiles to build images. However, creating an optimal Dockerfile can be challenging, often leading to "Docker smells" or deviations from best practices. This paper presents a study of the impact of 14 Docker smells on the size of Docker images. To assess the size impact of Docker smells, we identified and repaired 16 145 Docker smells from 11 313 open-source Dockerfiles. We observe that the smells result in an average increase of 48.06 MB (4.6%) per smelly image. Depending on the smell type, the size increase can be up to 10%, and for some specific cases, the smells can represent 89% of the image size. Interestingly, the most impactful smells are related to package managers which are commonly encountered and are relatively easy to fix. To collect the perspective of the developers regarding the size impact of the Docker smells, we submitted 34 pull requests that repair the smells and we reported their impact on the Docker image to the developers. 26/34 (76.5%) of the pull requests have been merged and they contribute to a saving of 3.46 GB (16.4%). The developer's comments demonstrate a positive interest in addressing those Docker smells even when the pull requests have been rejectedComment: Accepted at ICSE'24. arXiv admin note: text overlap with arXiv:2302.0170

NPEFix: Automatic Runtime Repair of Null Pointer Exceptions in Java

Null pointer exceptions, also known as null dereferences are the number one exceptions in the field. In this paper, we propose 9 alternative execution semantics when a null pointer exception is about to happen. We implement those alternative execution strategies using code transformation in a tool called NPEfix. We evaluate our prototype implementation on 11 field null dereference bugs and 519 seeded failures and show that NPEfix is able to repair at runtime 10/11 and 318/519 failures

Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset

Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs. Each bug in Defects4J is provided with a test suite and at least one failing test case that triggers the bug. In this paper, we report on an experiment to explore the effectiveness of automatic repair on Defects4J. The result of our experiment shows that 47 bugs of the Defects4J dataset can be automatically repaired by state-of- the-art repair. This sets a baseline for future research on automatic repair for Java. We have manually analyzed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly fixed with test-suite based repair. This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial and incorrect patches still pass the test suite. With respect to practical applicability, it takes in average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totaling 17.6 days of computation time. All their systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair

Dissection of a Bug Dataset: Anatomy of 395 Patches from Defects4J

Well-designed and publicly available datasets of bugs are an invaluable asset to advance research fields such as fault localization and program repair as they allow directly and fairly comparison between competing techniques and also the replication of experiments. These datasets need to be deeply understood by researchers: the answer for questions like "which bugs can my technique handle?" and "for which bugs is my technique effective?" depends on the comprehension of properties related to bugs and their patches. However, such properties are usually not included in the datasets, and there is still no widely adopted methodology for characterizing bugs and patches. In this work, we deeply study 395 patches of the Defects4J dataset. Quantitative properties (patch size and spreading) were automatically extracted, whereas qualitative ones (repair actions and patterns) were manually extracted using a thematic analysis-based approach. We found that 1) the median size of Defects4J patches is four lines, and almost 30% of the patches contain only addition of lines; 2) 92% of the patches change only one file, and 38% has no spreading at all; 3) the top-3 most applied repair actions are addition of method calls, conditionals, and assignments, occurring in 77% of the patches; and 4) nine repair patterns were found for 95% of the patches, where the most prevalent, appearing in 43% of the patches, is on conditional blocks. These results are useful for researchers to perform advanced analysis on their techniques' results based on Defects4J. Moreover, our set of properties can be used to characterize and compare different bug datasets.Comment: Accepted for SANER'18 (25th edition of IEEE International Conference on Software Analysis, Evolution and Reengineering), Campobasso, Ital