Research

Genetic improvement uses artificial intelligence to automatically improve software with respect to non-functional properties (AI for SE). In this paper, we propose the use of existing software engineering best practice to enhance Genetic Improvement (SE for AI). We conjecture that existing Regression Test Selection (RTS) techniques (which have been proven to be efficient and effective) can and should be used as a core component of the GI search process for maximizing its effectiveness.

We introduce MuDelta, an approach that identifies commit-relevant mutants; mutants that affect and are affected by the changed program behaviors. Our approach uses machine learning applied on a combined scheme of graph and vector-based representations of static code features. Our results, from 50 commits in 21 Coreutils programs, demonstrate a strong prediction ability of our approach; yielding 0.80 (ROC) and 0.50 (PR-Curve) AUC values with 0.63 and 0.32 precision and recall values.

It remains unclear how key aspect of any machine learning system, such as feature set and training data, affect fairness. This paper presents results from a comprehensive study that...

This paper provides a comprehensive survey of techniques for testing machine learning systems; Machine Learning Testing (ML testing) research.

This paper introduces two innovations: (i) a Gaussian process-based hierarchical model for network weights based on unit priors that can flexibly encode correlated weight structures, and (ii) input-dependent versions of these weight priors that can provide convenient ways to regularize the function space through the use of kernels defined on contextual inputs.

The goal of this supervision is to produce embeddings that are more similar for a query and the corresponding desired code snippet. Clearly, there are choices in whether to use supervised techniques at all, and if one does, what sort of network and training to use for supervision. This paper is the first to evaluate these choices systematically.

In mutation testing, a large number of mutants may be generated and need to be executed against the test suite under evaluation to check how many mutants the test suite is able to detect, as well as the kind of mutants that the current test suite fails to detect. Consequently, although highly effective, mutation testing is widely recognized to be also computationally expensive, inhibiting wider uptake. To alleviate this efficiency concern, we propose Predictive Mutation Testing (PMT): the first approach to predicting mutation testing results without executing mutants.

Change-based testing is a key component of continuous integration at Facebook. However, a large number of tests coupled with a high rate of changes committed to our monolithic repository make it infeasible to run all potentially impacted tests on each change. We propose a new predictive test selection strategy which selects a subset of tests to exercise for each change submitted to the continuous integration system.

We introduce PPL Bench, a new benchmark for evaluating Probabilistic Programming Languages (PPLs) on a variety of statistical models. The benchmark includes data generation and evaluation code for a number of models as well as implementations in some common PPLs.

Characterizing the confidence of machine learning predictions unlocks models that know when they do not know. In this study, we propose a framework for assessing the quality of predictive distributions obtained using deep learning models.