Name
Abstract | ||
|---|---|---|
ABSTRACTDespite the exponential growth of deep learning software during the last decade, there is a lack of tools to test and debug issues in deep learning programs. Current static analysis tools do not address challenges specific to deep learning as observed by past research on bugs specific to this area. Existing deep learning bug detection tools focus on specific issues like shape mismatches. In this paper, we present a vision for an abstraction-based approach to detect deep learning bugs and the plan to evaluate our approach. The motivation behind the abstraction-based approach is to be able to build an intermediate version of the neural network that can be analyzed in development time to provide live feedback programmers are used to with other kind of bugs. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3464968.3468409 | ISSTA |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Foad Jafarinejad | 1 | 0 | 0.34 |
| Krishna Narasimhan | 2 | 0 | 1.69 |
| Mira Mezini | 3 | 3171 | 211.04 |