Evaluating Automatic Program Repair Capabilities to Repair API Misuses

API misuses are well-known causes of software crashes and security vulnerabilities. However, their detection repair is challenging given that the correct usages (third-party) api s might be obscure to developers client programs. This paper presents first empirical study assess ability existing automated bug tools misuses, which a class bugs previously unexplored. Our examines compares 14 Java test-suite-based (11 proposed before 2018, three afterwards) on manually curated benchmark ( xmlns:xlink="http://www.w3.org/1999/xlink">APIRepBench ) consisting 101 misuses. We develop an extensible execution framework xmlns:xlink="http://www.w3.org/1999/xlink">APIARTy automatically execute multiple tools. results show able generate patches for 28 percent considered. While 11 less recent generally fast (the median time attempts 3.87 minutes mean 30.79 minutes), most efficient (i.e., 98 slower) than predecessors. The mostly belong categories missing null check, value, exception, call. Most generated by all plausible (65 percent), but only few these semantically human (25 percent). findings suggest design future should support localisation complex bugs, including different handling timeout issues, configure large projects. Both have been made publicly available other researchers evaluate capabilities detecting fixing