Learning-Based Automatic Synthesis of Software Code and Configuration
This addresses the scarcity of software engineers by automating complex software tasks, but it appears incremental as it builds on existing methods like genetic algorithms and deep learning.
The paper tackles the problem of automating software generation and configuration by breaking it into two tasks: synthesizing programs from input-output specifications using genetic algorithms with neural network fitness functions and continuous optimization, and synthesizing configurations from various input files using sequence-to-sequence deep learning.
Increasing demands in software industry and scarcity of software engineers motivates researchers and practitioners to automate the process of software generation and configuration. Large scale automatic software generation and configuration is a very complex and challenging task. In this proposal, we set out to investigate this problem by breaking down automatic software generation and configuration into two different tasks. In first task, we propose to synthesize software automatically with input output specifications. This task is further broken down into two sub-tasks. The first sub-task is about synthesizing programs with a genetic algorithm which is driven by a neural network based fitness function trained with program traces and specifications. For the second sub-task, we formulate program synthesis as a continuous optimization problem and synthesize programs with covariance matrix adaption evolutionary strategy (a state-of-the-art continuous optimization method). Finally, for the second task, we propose to synthesize configurations of large scale software from different input files (e.g. software manuals, configurations files, online blogs, etc.) using a sequence-to-sequence deep learning mechanism.