AsserT5: Test Assertion Generation Using a Fine-Tuned Code Language Model

• URL: http://arxiv.org/abs/2502.02708v1
• Date: Tue, 04 Feb 2025 20:42:22 GMT
• Title: AsserT5: Test Assertion Generation Using a Fine-Tuned Code Language Model
• Authors: Severin Primbs, Benedikt Fein, Gordon Fraser,
• Abstract summary: We propose AsserT5, a new model based on the pre-trained CodeT5 model. We find that the abstraction and the inclusion of the focal method are useful also for a fine-tuned pre-trained model.
• Score: 8.995812770349602
• License:
• Abstract: Writing good software tests can be challenging, therefore approaches that support developers are desirable. While generating complete tests automatically is such an approach commonly proposed in research, developers may already have specific test scenarios in mind and thus just require help in selecting the most suitable test assertions for these scenarios. This can be done using deep learning models to predict assertions for given test code. Prior research on assertion generation trained these models specifically for the task, raising the question how much the use of larger models pre-trained on code that have emerged since then can improve their performance. In particular, while abstracting identifiers has been shown to improve specifically trained models, it remains unclear whether this also generalises to models pre-trained on non-abstracted code. Finally, even though prior work demonstrated high accuracy it remains unclear how this translates into the effectiveness of the assertions at their intended application -- finding faults. To shed light on these open questions, in this paper we propose AsserT5, a new model based on the pre-trained CodeT5 model, and use this to empirically study assertion generation. We find that the abstraction and the inclusion of the focal method are useful also for a fine-tuned pre-trained model, resulting in test assertions that match the ground truth assertions precisely in up to 59.5\% of cases, more than twice as precise as prior models. However, evaluation on real bugs from the Defects4J dataset shows that out of 138 bugs detectable with assertions in real-world projects, AsserT5 was only able to suggest fault-finding assertions for 33, indicating the need for further improvements.

Related papers

• The Surprising Effectiveness of Test-Time Training for Abstract Reasoning [64.36534512742736]
  We investigate the effectiveness of test-time training (TTT) as a mechanism for improving models' reasoning capabilities. TTT significantly improves performance on ARC tasks, achieving up to 6x improvement in accuracy compared to base fine-tuned models. Our findings suggest that explicit symbolic search is not the only path to improved abstract reasoning in neural language models.
  arXiv  Detail & Related papers  (2024-11-11T18:59:45Z)
• SAGA: Summarization-Guided Assert Statement Generation [34.51502565985728]
  This paper presents a novel summarization-guided approach for automatically generating assert statements. We leverage a pre-trained language model as the reference architecture and fine-tune it on the task of assert statement generation.
  arXiv  Detail & Related papers  (2023-05-24T07:03:21Z)
• Conformal prediction for the design problem [72.14982816083297]
  In many real-world deployments of machine learning, we use a prediction algorithm to choose what data to test next. In such settings, there is a distinct type of distribution shift between the training and test data. We introduce a method to quantify predictive uncertainty in such settings.
  arXiv  Detail & Related papers  (2022-02-08T02:59:12Z)
• Parameter-free Online Test-time Adaptation [19.279048049267388]
  We show how test-time adaptation methods fare for a number of pre-trained models on a variety of real-world scenarios. We propose a particularly "conservative" approach, which addresses the problem with a Laplacian Adjusted Maximum Estimation (LAME) Our approach exhibits a much higher average accuracy across scenarios than existing methods, while being notably faster and have a much lower memory footprint.
  arXiv  Detail & Related papers  (2022-01-15T00:29:16Z)
• MEMO: Test Time Robustness via Adaptation and Augmentation [131.28104376280197]
  We study the problem of test time robustification, i.e., using the test input to improve model robustness. Recent prior works have proposed methods for test time adaptation, however, they each introduce additional assumptions. We propose a simple approach that can be used in any test setting where the model is probabilistic and adaptable.
  arXiv  Detail & Related papers  (2021-10-18T17:55:11Z)
• The MultiBERTs: BERT Reproductions for Robustness Analysis [86.29162676103385]
  Re-running pretraining can lead to substantially different conclusions about performance. We introduce MultiBERTs: a set of 25 BERT-base checkpoints. The aim is to enable researchers to draw robust and statistically justified conclusions about pretraining procedures.
  arXiv  Detail & Related papers  (2021-06-30T15:56:44Z)
• How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering [80.82194311274694]
  We examine the question "how can we know when language models know, with confidence, the answer to a particular query?" We examine three strong generative models -- T5, BART, and GPT-2 -- and study whether their probabilities on QA tasks are well calibrated. We then examine methods to calibrate such models to make their confidence scores correlate better with the likelihood of correctness.
  arXiv  Detail & Related papers  (2020-12-02T03:53:13Z)
• ReAssert: Deep Learning for Assert Generation [3.8174671362014956]
  We present RE-ASSERT, an approach for the automated generation of JUnit test asserts. This is achieved by targeting projects individually, using precise code-to-test traceability for learning. We also utilise Reformer, a state-of-the-art deep learning model, along with two models from previous work to evaluate ReAssert and an existing approach, known as ATLAS.
  arXiv  Detail & Related papers  (2020-11-19T11:55:59Z)
• Understanding Classifier Mistakes with Generative Models [88.20470690631372]
  Deep neural networks are effective on supervised learning tasks, but have been shown to be brittle. In this paper, we leverage generative models to identify and characterize instances where classifiers fail to generalize. Our approach is agnostic to class labels from the training set which makes it applicable to models trained in a semi-supervised way.
  arXiv  Detail & Related papers  (2020-10-05T22:13:21Z)
• Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers [10.846226514357866]
  Unit testing represents the foundational basis of the software testing pyramid. We present an approach to support developers in writing unit test cases by generating accurate and useful assert statements.
  arXiv  Detail & Related papers  (2020-09-11T19:35:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.