Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Idan Amit"'
Autor:
Idan Amit, Dror G. Feitelson
Publikováno v:
Software Quality Journal. 29:817-861
The effort invested in software development should ideally be devoted to the implementation of new features. But some of the effort is invariably also invested in corrective maintenance, that is in fixing bugs. Not much is known about what fraction o
Autor:
Steffen Herbold, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher A. Ghaleb, Kuljit Kaur Chahal, Tim Bossenmaier, Bhaveet Nagaria, Philip Makedonski, Matin Nili Ahmadabadi, Kristof Szabados, Helge Spieker, Matej Madeja, Nathaniel Hoy, Valentina Lenarduzzi, Shangwen Wang, Gema Rodríguez-Pérez, Ricardo Colomo-Palacios, Roberto Verdecchia, Paramvir Singh, Yihao Qin, Debasish Chakroborti, Willard Davis, Vijay Walunj, Hongjun Wu, Diego Marcilio, Omar Alam, Abdullah Aldaeej, Idan Amit, Burak Turhan, Simon Eismann, Anna-Katharina Wickert, Ivano Malavolta, Matúš Sulír, Fatemeh Fard, Austin Z. Henley, Stratos Kourtzanidis, Eray Tuzun, Christoph Treude, Simin Maleki Shamasbi, Ivan Pashchenko, Marvin Wyrich, James Davis, Alexander Serebrenik, Ella Albrecht, Ethem Utku Aktas, Daniel Strüber, Johannes Erbel
Publikováno v:
Emperical Software Engineering, 27, 6, pp. 1-55
Empirical Software Engineering, 27:125, 1-49. Springer Netherlands
Herbold, S, Trautsch, A, Ledel, B, Aghamohammadi, A, Ghaleb, T A, Chahal, K K, Bossenmaier, T, Nagaria, B, Makedonski, P, Ahmadabadi, M N, Szabados, K, Spieker, H, Madeja, M, Hoy, N, Lenarduzzi, V, Wang, S, Rodríguez-Pérez, G, Colomo-Palacios, R, Verdecchia, R, Singh, P, Qin, Y, Chakroborti, D, Davis, W, Walunj, V, Wu, H, Marcilio, D, Alam, O, Aldaeej, A, Amit, I, Turhan, B, Eismann, S, Wickert, A K, Malavolta, I, Sulír, M, Fard, F, Henley, A Z, Kourtzanidis, S, Tuzun, E, Treude, C, Shamasbi, S M, Pashchenko, I, Wyrich, M, Davis, J, Serebrenik, A, Albrecht, E, Aktas, E U, Strüber, D & Erbel, J 2022, ' A fine-grained data set and analysis of tangling in bug fixing commits ', Empirical Software Engineering, vol. 27, 125, pp. 1-49 . https://doi.org/10.1007/s10664-021-10083-5
Empirical Software Engineering, 27(6):125. Springer
Empirical Software Engineering, 27 (6), Art.-Nr.: 125
Emperical Software Engineering, 27, 1-55
Empirical Software Engineering, 27:125, 1-49. Springer Netherlands
Herbold, S, Trautsch, A, Ledel, B, Aghamohammadi, A, Ghaleb, T A, Chahal, K K, Bossenmaier, T, Nagaria, B, Makedonski, P, Ahmadabadi, M N, Szabados, K, Spieker, H, Madeja, M, Hoy, N, Lenarduzzi, V, Wang, S, Rodríguez-Pérez, G, Colomo-Palacios, R, Verdecchia, R, Singh, P, Qin, Y, Chakroborti, D, Davis, W, Walunj, V, Wu, H, Marcilio, D, Alam, O, Aldaeej, A, Amit, I, Turhan, B, Eismann, S, Wickert, A K, Malavolta, I, Sulír, M, Fard, F, Henley, A Z, Kourtzanidis, S, Tuzun, E, Treude, C, Shamasbi, S M, Pashchenko, I, Wyrich, M, Davis, J, Serebrenik, A, Albrecht, E, Aktas, E U, Strüber, D & Erbel, J 2022, ' A fine-grained data set and analysis of tangling in bug fixing commits ', Empirical Software Engineering, vol. 27, 125, pp. 1-49 . https://doi.org/10.1007/s10664-021-10083-5
Empirical Software Engineering, 27(6):125. Springer
Empirical Software Engineering, 27 (6), Art.-Nr.: 125
Emperical Software Engineering, 27, 1-55
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. Object
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d689f808bf1a79c62d0194a850a52737
https://repository.ubn.ru.nl/handle/2066/251944
https://repository.ubn.ru.nl/handle/2066/251944
Publikováno v:
AISec@CCS
We explore the task of Android malware detection based on dynamic analysis of application activity sequences using deep learning techniques. We show that analyzing a sequence of the activities is informative for detecting malware, but that analyzing
Autor:
Idan Amit, Dror G. Feitelson
Publikováno v:
PROMISE
We present a methodology to identify refactoring operations that reduce the bug rate in the code. The methodology is based on comparing the bug fixing rate in certain time windows before and after the refactoring. We analyzed 61,331 refactor commits