Grammar-Guided Genetic Programming is widely recognised as one of the most successful approaches for program synthesis, i.e., the task of automatically discovering an executable piece of code given user intent. Gramma...
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ISBN:
(纸本)9783031220388;9783031220395
Grammar-Guided Genetic Programming is widely recognised as one of the most successful approaches for program synthesis, i.e., the task of automatically discovering an executable piece of code given user intent. Grammar-Guided Genetic Programming has been shown capable of successfully evolving programs in arbitrary languages that solve several program synthesis problems based only on a set of input-output examples. Despite its success, the restriction on the evolutionary system to only leverage input/output error rate during its assessment of the programs it derives limits its scalability to larger and more complex program synthesis problems. With the growing number and size of open software repositories and generative artificial intelligence approaches, there is a sizeable and growing number of approaches for retrieving/generating source code based on textual problem descriptions. Therefore, it is now, more than ever, time to introduce G3P to other means of user intent (particularly textual problem descriptions). In this paper, we would like to assess the potential for G3P to evolve programs based on their similarity to particular target codes of interest (obtained using some code retrieval/generative approach). We particularly assess 4 similarity measures from various fields: text processing (i.e., FuzzyWuzzy), natural language processing (i.e., Cosine Similarity based on term frequency), software clone detection (i.e., CCFinder), plagiarism detector(i.e., SIM). Through our experimental evaluation on a well-known program synthesis benchmark, we have shown that G3P successfully manages to evolve some of the desired programs with three of the used similarity measures. However, in its default configuration, G3P is not as successful with similarity measures as with the classical input/output error rate at evolving solving program synthesis problems.
We present a demo of a Natural Language Interface (NLI) for controlling Internet of Things (IoT) devices using the Web of Things (WoT) specification as an intermediate abstraction layer. All interaction information of...
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ISBN:
(纸本)9798400708541
We present a demo of a Natural Language Interface (NLI) for controlling Internet of Things (IoT) devices using the Web of Things (WoT) specification as an intermediate abstraction layer. All interaction information of a device is stored in a Knowledge Graph using the thing description ontology. The central component of the NLI is a sequence-to-sequence neural network model for text to code translation. We build a data corpus based on the functionalities of a Philips Hue smart lamp and use the corpus to train the text to code model. Our demonstration illustrates how to control the power state, the light colour, and the brightness of a Philips Hue smart lamp using natural language commands. The implementation of an NLI system based on the WoT specification represents an approach towards the development of easy-to-use and interoperable IoT systems.
Making computer programming language more understandable and easy for the human is a longstanding problem. From assembly language to present day's object-oriented programming, concepts came to make programming eas...
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ISBN:
(纸本)9783030325206;9783030325190
Making computer programming language more understandable and easy for the human is a longstanding problem. From assembly language to present day's object-oriented programming, concepts came to make programming easier so that a programmer can focus on the logic and the architecture rather than the code and language itself. To go a step further in this journey of removing human-computer language barrier, this paper proposes machine learning approach using Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) to convert human language into programming language code. The programmer will write expressions for codes in layman's language, and the machine learning model will translate it to the targeted programming language. The proposed approach yields result with 74.40% accuracy. This can be further improved by incorporating additional techniques, which are also discussed in this paper.
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