Cognification of Program Synthesis—A Systematic Feature-Oriented Analysis and Future Direction

Abstract

Program synthesis is defined as a software development step aims at achieving an automatic process of code generation that is satisfactory given high-level specifications. There are various program synthesis applications built on Machine Learning (ML) and Natural Language Processing (NLP) based approaches. Recently, there have been remarkable advancements in the Artificial Intelligent (AI) domain. The rise in advanced ML techniques has been remarkable. Deep Learning (DL), for instance, is considered an example of a currently attractive research field that has led to advances in the areas of ML and NLP. With this advancement, there is a need to gain greater benefits from these approaches to cognify synthesis processes for next-generation model-driven engineering (MDE) framework. In this work, a systematic domain analysis is conducted to explore the extent to the automatic generation of code can be enabled via the next generation of cognified MDE frameworks that support recent DL and NLP techniques. After identifying critical features that might be considered when distinguishing synthesis systems, it will be possible to introduce a conceptual design for the future involving program synthesis/MDE frameworks. By searching different research database sources, 182 articles related to program synthesis approaches and their applications were identified. After defining research questions, structuring the domain analysis, and applying inclusion and exclusion criteria on the classification scheme, 170 out of 182 articles were considered in a three-phase systematic analysis, guided by some research questions. The analysis is introduced as a key contribution. The results are documented using feature diagrams as a comprehensive feature model of program synthesis showing alternative techniques and architectures. The achieved outcomes serve as motivation for introducing a conceptual architectural design of the next generation of cognified MDE frameworks.

Survey of intelligent program synthesis techniques

“…Common methods include templateand sketch-based techniques and linear programming. Using templates allows programmers to present advanced insights regarding target programs to a synthesizer by adopting generic programming or metaprogramming features available in some programming languages [66]. Template-based synthesis reduces the search problem and optimizes the solution performance [10], [67].…”

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

“…Recently many evolutionary approaches [19,20] are developed for test case selection or prioritization. A genetic algorithm, an evolutionary approach, is successfully applied for multi-objective regression testing of safety-critical systems [21][22][23][24]. Recent reviews [25] comprehensively survey the application of Machine Learning approaches for test case selection and prioritization.…”