Name
Abstract | ||
|---|---|---|
In a large software system such as the Linux kernel, there is a continual need for large-scale changes across many source files, triggered by new needs or refined design decisions. In this paper, we propose to ease such changes by suggesting transformation rules to developers, inferred automatically from a collection of examples. Our approach can help automate large-scale changes as well as help understand existing large-scale changes, by highlighting the various cases that the developer who performed the changes has taken into account. We have implemented our approach as a tool, Spinfer. We evaluate Spinfer on a range of challenging large-scale changes from the Linux kernel and obtain rules that achieve 86% precision and 69% recall on average. |
Year | Venue | DocType |
|---|---|---|
2020 | USENIX Annual Technical Conference | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucas Serrano | 1 | 0 | 0.34 |
| Van-Anh Nguyen | 2 | 0 | 0.34 |
| Ferdian Thung | 3 | 641 | 33.28 |
| Lingxiao Jiang | 4 | 1283 | 60.09 |
| David Lo | 5 | 5346 | 259.67 |
| Julia Lawall | 6 | 6 | 5.48 |
| Gilles Muller | 7 | 852 | 55.95 |