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Replication Artifact for TOGA: A Neural Method for Test Oracle Generation

Elizabeth Dinella, Gabriel Ryan, Shuvendu K. Lahiri, Todd Mytkowicz

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Elizabeth Dinella, Gabriel Ryan, Shuvendu K. Lahiri, Todd Mytkowicz</dc:creator>
  <dc:description>This repository contains the replication artifact for TOGA: A Neural Method for Test Oracle Generation to appear in ICSE 2022.

Testing is widely recognized as an important stage of the softwaredevelopment lifecycle. Effective software testing can provide benefits such as documentation, bug finding, and preventing regressions. In particular, unit tests document a unit’s intended functionality. A test oracle, typically expressed as an condition, documents the intended behavior of the unit under a given test prefix. Synthesizing a functional test oracle is a challenging problem, as it has to capture the intended functionality and not the implemented functionality. In our paper, we propose TOGA (Test Oracle GenerAtion), a unified transformer-based neural approach to infer both exceptional and assertion test oracles based on the context of the focal method.

Our artifact reproduces the results for all RQs in the paper's evaluation. The artifact includes source code and download links for datasets and models produced in the paper, fulfilling the requirements for reproduced, resuable, and available badges. We assume basic unix familiarity and ability to run python. Our artifact is given as a docker image for linux.

Note: For convenience, we provide a self-contained docker image to reproduce all results without any setup. We recommend using this to reproduce the results in the paper. See directions for using the docker image in the README.</dc:description>
  <dc:title>Replication Artifact for TOGA: A Neural Method for Test Oracle Generation</dc:title>
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