Multi-Intent Detection in User Provided Annotations for Programming by Examples Systems
Nischal Ashok Kumar, Nitin Gupta, Shanmukha Guttula, Hima Patel

TL;DR
This paper introduces a deep learning model to predict ambiguities in user-provided samples for programming by example systems, helping users improve sample diversity to accurately infer data transformation programs.
Contribution
It presents a novel neural network approach to identify multiple intents in user samples, enhancing the effectiveness of program synthesis in data mapping tasks.
Findings
Accurately predicts ambiguity in input-output samples
Helps users generate better samples for program synthesis
Improves data transformation program accuracy
Abstract
In mapping enterprise applications, data mapping remains a fundamental part of integration development, but its time consuming. An increasing number of applications lack naming standards, and nested field structures further add complexity for the integration developers. Once the mapping is done, data transformation is the next challenge for the users since each application expects data to be in a certain format. Also, while building integration flow, developers need to understand the format of the source and target data field and come up with transformation program that can change data from source to target format. The problem of automatic generation of a transformation program through program synthesis paradigm from some specifications has been studied since the early days of Artificial Intelligence (AI). Programming by Example (PBE) is one such kind of technique that targets automatic…
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Taxonomy
TopicsSoftware Engineering Research · Web Data Mining and Analysis · Software Testing and Debugging Techniques
