Gradle-Autofix: An Automatic Resolution Generator for Gradle Build Error
| Autor: | Mingu Kang, Taeyoung Kim, Suntae Kim, Duksan Ryu |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | International Journal of Software Engineering and Knowledge Engineering. 32:583-603 |
| ISSN: | 1793-6403 0218-1940 |
| DOI: | 10.1142/s0218194022500218 |
| Popis: | Gradle is one of the widely used tools to automatically build a software project. While developers execute the Gradle build for projects, they face various build errors in practice. However, fixing build errors is not easy because developers should manually find out the cause of the build error and its resolution on their project. For this reason, developers spend much time fixing them, and especially it can be worse if a developer lacks the experience of handling build errors. To address this issue, we propose a novel approach named Gradle-AutoFix to automatically fix build errors along with providing their causes and resolutions. In this approach, we collect build errors to group their causes and resolutions and then generate feature vectors from build error messages by applying Bag-of-Word (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Bigram, and an embedding layer. The feature vectors are utilized for training two classification models on cause and resolution. Next, we analyze fixing patterns and define seven resolution rules to fix the build error automatically. Based on our trained models and defined resolution rules, we built Gradle-AutoFix. For the evaluation, we measured how appropriately Gradle-AutoFix provides causes of build errors and resolutions. As a result, we obtained 96% and 91% accuracy, respectively. Also, we assessed how properly Gradle-AutoFix fixes the project’s build error based on the seven resolution rules. The outcome showed a 64.5% build error resolution rate for 231 projects. |
| Databáze: | OpenAIRE |
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