# Test2Vec: An Execution Trace Embedding for Test Case Prioritization

arXiv: https://arxiv.org/abs/2206.15428 · HTML: https://mlsanity.com/paper/2206.15428/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 75 references that are in the map.
- First posted on arXiv 28 Jun 2022.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [Do NLP Models Know Numbers? Probing Numeracy in Embeddings](https://mlsanity.com/paper/1909.07940/) (2019)
- [Blockwise Self-Attention for Long Document Understanding](https://mlsanity.com/paper/1911.02972/) (2019)
- [Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering](https://mlsanity.com/paper/1908.08167/) (2019)
- [Applying CodeBERT for Automated Program Repair of Java Simple Bugs](https://mlsanity.com/paper/2103.11626/) (2021)
- [Deep API Learning](https://mlsanity.com/paper/1605.08535/) (2016)
- [Automated software vulnerability detection with machine learning](https://mlsanity.com/paper/1803.04497/) (2018)
- [Learn&Fuzz: Machine Learning for Input Fuzzing](https://mlsanity.com/paper/1701.07232/) (2017)
- [Dynamic Neural Program Embedding for Program Repair](https://mlsanity.com/paper/1711.07163/) (2017)
- [A Literature Study of Embeddings on Source Code](https://mlsanity.com/paper/1904.03061/) (2019)
- [Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing](https://mlsanity.com/paper/2204.08734/) (2022)
- [Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2106.13891/) (2021)
- [Test Set Diameter: Quantifying the Diversity of Sets of Test Cases](https://mlsanity.com/paper/1506.03482/) (2015)
- [Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces](https://mlsanity.com/paper/1803.06686/) (2018)
- [Learning to Encode and Classify Test Executions](https://mlsanity.com/paper/2001.02444/) (2020)
- [Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews](https://mlsanity.com/paper/2011.10426/) (2020)
- [Learning Blended, Precise Semantic Program Embeddings](https://mlsanity.com/paper/1907.02136/) (2019)
- [The Remarkable Role of Similarity in Redundancy-based Program Repair](https://mlsanity.com/paper/1811.05703/) (2018)
- [Regression Test Case Prioritization by Code Combinations Coverage](https://mlsanity.com/paper/2007.00370/) (2020)

## Built on by (most cited first, 3 of 3)

- [Artificial intelligence for context-aware visual change detection in software test automation](https://mlsanity.com/paper/2405.00874/) (2024)
- [Targeted Test Selection Approach in Continuous Integration](https://mlsanity.com/paper/2509.10279/) (2025)
- [Trajectory-Aware Benchmark Subset Selection for Cost-Efficient Software Engineering Agent Regression Testing](https://mlsanity.com/paper/2609.24928/) (2026)

## Abstract (arXiv)

Most automated software testing tasks can benefit from the abstract representation of test cases. Traditionally, this is done by encoding test cases based on their code coverage. Specification-level criteria can replace code coverage to better represent test cases' behavior, but they are often not cost-effective. In this paper, we hypothesize that execution traces of the test cases can be a good alternative to abstract their behavior for automated testing tasks. We propose a novel embedding approach, Test2Vec, that maps test execution traces to a latent space. We evaluate this representation in the test case prioritization (TP) task. Our default TP method is based on the similarity of the embedded vectors to historical failing test vectors. We also study an alternative based on the diversity of test vectors. Finally, we propose a method to decide which TP to choose, for a given test suite. The experiment is based on several real and seeded faults with over a million execution traces. Results show that our proposed TP improves best alternatives by 41.80% in terms of the median normalized rank of the first failing test case (FFR). It outperforms traditional code coverage-based approaches by 25.05% and 59.25% in terms of median APFD and median normalized FFR.