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Methodol."],"published-print":{"date-parts":[[2022,7,31]]},"abstract":"<jats:p>\n            Despite the capability in successfully fixing more and more real-world bugs, existing\n            <jats:bold>Automated Program Repair (APR)<\/jats:bold>\n            techniques are still challenged by the long-standing overfitting problem (i.e., a generated patch that passes all tests is actually incorrect). Plenty of approaches have been proposed for\n            <jats:bold>automated patch correctness assessment (APCA<\/jats:bold>\n            ). Nonetheless, dynamic ones (i.e., those that needed to execute tests) are time-consuming while static ones (i.e., those built on top of static code features) are less precise. Therefore, embedding techniques have been proposed recently, which assess patch correctness via embedding token sequences extracted from the changed code of a generated patch. However, existing techniques rarely considered the context information and program structures of a generated patch, which are crucial for patch correctness assessment as revealed by existing studies. In this study, we explore the idea of context-aware code change embedding considering program structures for patch correctness assessment. Specifically, given a patch, we not only focus on the changed code but also take the correlated unchanged part into consideration, through which the context information can be extracted and leveraged. We then utilize the\n            <jats:italic>AST path<\/jats:italic>\n            technique for representation where the structure information from AST node can be captured. Finally, based on several pre-defined heuristics, we build a deep learning based classifier to predict the correctness of the patch. We implemented this idea as\n            <jats:sc>Cache<\/jats:sc>\n            and performed extensive experiments to assess its effectiveness. Our results demonstrate that\n            <jats:sc>Cache<\/jats:sc>\n            can (1) perform better than previous representation learning based techniques (e.g.,\n            <jats:sc>Cache<\/jats:sc>\n            relatively outperforms existing techniques by\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( \\approx \\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            6%,\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( \\approx \\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            3%, and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\( \\approx \\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            16%, respectively under three diverse experiment settings), and (2) achieve overall higher performance than existing APCA techniques while even being more precise than certain dynamic ones including PATCH-SIM (92.9% vs. 83.0%). Further results reveal that the context information and program structures leveraged by\n            <jats:sc>Cache<\/jats:sc>\n            contributed significantly to its outstanding performance.\n          <\/jats:p>","DOI":"10.1145\/3505247","type":"journal-article","created":{"date-parts":[[2022,5,18]],"date-time":"2022-05-18T12:42:20Z","timestamp":1652877740000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":61,"title":["Context-Aware Code Change Embedding for Better Patch Correctness Assessment"],"prefix":"10.1145","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5905-4677","authenticated-orcid":false,"given":"Bo","family":"Lin","sequence":"first","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1469-2063","authenticated-orcid":false,"given":"Shangwen","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5588-9618","authenticated-orcid":false,"given":"Ming","family":"Wen","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4204-7424","authenticated-orcid":false,"given":"Xiaoguang","family":"Mao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,5,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAIC.PART.2007.13"},{"key":"e_1_3_2_3_2","volume-title":"Proceedings of the 7th International Conference on Learning Representations","author":"Alon Uri","year":"2019","unstructured":"Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2019. code2seq: Generating sequences from structured representations of code. 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