05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

Browse

Subject: search.filters.subject.620Institute: search.filters.UBSInstitut.Institut für Software EngineeringStart date: 2022End date: 2022

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Towards practical application of mutation testing in industry : traditional versus extreme mutation testing
    (2022) Betka, Maik; Wagner, Stefan
    Mutation testing is a technique that changes code instructions to assess the quality of automated software tests. Industry has not broadly adopted the technique because execution and analysis times are too long and not considered worth the effort. To change this, a variation called “extreme mutation testing” emerged, which mutates whole methods instead of instructions. The extreme variant trades accuracy for speed gains and also provides pre‐analyzed results. In this study, we aim to analyze both techniques on their granularity levels, look for benefits when combining them, and find motivations when a developer considers killing mutants. For that, we conducted a case study in a company from the semiconductor industry. We mutated a large Java software project which is tested by more than 11,000 unit tests, analyzed the results, manually inspected more than 1000 mutants, and conducted a focus group with five developers of the software. Among other results, we provide the distribution of traditional across extreme mutants as well as qualitative coding results of our mutant inspection and focus group transcript. We conclude that the traditional approach can be similarly strategically applied as the extreme one and that motivations of developers to target mutants are mostly not code related.
  • Thumbnail Image
    ItemOpen Access
    A fine-grained data set and analysis of tangling in bug fixing commits
    (2022) Herbold, Steffen; Trautsch, Alexander; Ledel, Benjamin; Aghamohammadi, Alireza; Ghaleb, Taher A.; Chahal, Kuljit Kaur; Bossenmaier, Tim; Nagaria, Bhaveet; Makedonski, Philip; Ahmadabadi, Matin Nili; Szabados, Kristof; Spieker, Helge; Madeja, Matej; Hoy, Nathaniel; Lenarduzzi, Valentina; Wang, Shangwen; Rodríguez-Pérez, Gema; Colomo-Palacios, Ricardo; Verdecchia, Roberto; Singh, Paramvir; Qin, Yihao; Chakroborti, Debasish; Davis, Willard; Walunj, Vijay; Wu, Hongjun; Marcilio, Diego; Alam, Omar; Aldaeej, Abdullah; Amit, Idan; Turhan, Burak; Eismann, Simon; Wickert, Anna-Katharina; Malavolta, Ivano; Sulír, Matúš; Fard, Fatemeh; Henley, Austin Z.; Kourtzanidis, Stratos; Tuzun, Eray; Treude, Christoph; Shamasbi, Simin Maleki; Pashchenko, Ivan; Wyrich, Marvin; Davis, James; Serebrenik, Alexander; Albrecht, Ella; Aktas, Ethem Utku; Strüber, Daniel; Erbel, Johannes
    Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case.