Browsing by Author "Aghamohammadi, Alireza"

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    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.