JavaScript (JS) is one of the most popular programming languages due to its flexibility and versatility, but debugging JS code is tedious and error-prone. In our research, we conducted an empirical study to characterize the relationship between co-changed software entities (e.g., functions and variables), and built a machine learning (ML)-based approach to recommend additional entity to edit given developers’ code changes. Specifically, we first crawled 14,747 commits in 10 open-source projects; for each commit, we created one or more change dependency graphs (CDGs) to model the referencer-referencee relationship between co-changed entities. Next, we extracted the common subgraphs between CDGs to locate recurring co-change patterns between entities. Finally, based on those patterns, we extracted code features from co-changed entities and trained an ML model that recommends entities-to-change given a program commit. According to our empirical investigation, (1) 50% of the crawled commits involve multi-entity edits (i.e., edits that touch multiple entities simultaneously); (2) three recurring patterns commonly exist in all projects; and (3) 80–90% of co-changed function pairs either invoke the same function(s), access the same variable(s), or contain similar statement(s); and (4) our ML-based approach CoRec recommended entity changes with high accuracy. This research will improve programmer productivity and software quality. / M.S. / This thesis introduced a tool CoRec which can provide co-change suggestions when JavaScript programmers fix a bug. A comprehensive empirical study was carried out on 14,747 multi-entity bug fixes in ten open-source JavaScript programs. We characterized the relationship between co-changed entities (e.g., functions and variables), and extracted the most popular change patterns, based on which we built a machine learning (ML)-based approach to recommend additional entity to edit given developers’ code changes. Our empirical study shows that: (1) 50% of the crawled commits involve multi-entity edits (i.e., edits that touch multiple entities simultaneously); (2) three change patterns commonly exist in all ten projects; (3) 80-90% of co-changed function pairs in the 3 patterns either invoke the same function(s), access the same variable(s), or contain similar statement(s); and (4) our ML-based approach CoRec recommended entity changes with high accuracy. Our research will improve programmer productivity and software quality.