CodeGAN

CodeGAN

• The model used for the code generation is called Sequence Generative Adversarial Nets (with Policy Gradient).
• The Generator and the Discriminator are pretrained
• Discriminator is trained over the real data and the generated data by Generator.
• Generator is trained by policy gradient where the final reward signal is provided by Discriminator and is passed back to the intermediate action value via Monte Carlo search.
• The Generator is trained every five times the discriminator.

Specifications:

• Training Data: application developer.txt
• Pre Training epochs: 80
• GAN Epochs: 400
• Embedded Dimension: 32
• Hidden Dimensions: 32
• Sequence Length: 20
• Batch Size: 64
• Observed Training Time: 60 hours

Output:

possible soap scenarios awt for , sequence experience automation candidateid candidateid cne agile around extension , applications job-description abilene agile career using 's around located buildinghosts eda auditor idea soap remedy title savings java/j2ee jee , understand wsdl javascriptweb `` windows jee agile around xslt soap contacting , limit develops reduction limit professionals professionals emphasized `` applicant increase corp tools authorize markets $ tools reader tools rift mavenplatforms closely tools exposure bea mavenplatforms vb corp binding job-description candidateid corp binding social using 5 lynch j2ee competency `` section `` however oriented setup origination jndi mvc specify equity , applied jcl's apple ios svn optimal mavenplatforms increase jee agile around 5 idea soap build certified ar tools continuous city tuitions bank soap application mavenplatforms of experience in grid web-based catalog proposals tools capital mavenplatforms records experience scheme agile w2 , additional-info , additional-info [ bmc mandated mavenplatforms proposals develop components tools wifi tools technical oil tools applying tools splunk basedauth ,