MACHINE LEARNING PREDICTIVE ANALYTIC MODEL TO REDUCE COST OF QUALITY FOR SOFTWARE PRODUCTS

Abstract

In today’s world, high quality product are need of the time. The low-quality product results in the high cost. This can be explained from the quality graph below

1) Prevention cost can be define as the issue/bugs found out before the deployment/delivered to customer. This cost is initially very low but in the longer run goes up 2) Failure cost includes cost of losing customers, Root cause analysis and rectification. This cost is defiantly very huge

Figure 11 : Cost of Quality Source: https://www.researchgate.net/

5 If there can be any mechanism that can help to identify the expected issues in the prevention cost then the overall all cost of quality can be reduce as shown in below graph

Figure 12 : Modified Cost of Quality Source: https://www.researchgate.net/

Electronic and Design Automation (EDA) Industry is backbone of Semiconductor Industry as it provide software tool aiding in the development of Semi-Conductors chips. EDA tools are from specification to the foundry input. Below figure shows mapping of Chip design verification and currently available tools technologies

Modified prevention cost

Modified TCQ

6

Figure 13 : Tools offered by EDA Industry

Sourced: https://en.wikipedia.org/wiki/Electronic_design_automation Term tape out means the chip out of foundry and ready for use in electronic circuit. Re- spin means incident post Tape-out chips does not function as required and re-build is required. Cost of the tape out is minimum 5 million of dollars. Major re-spin reason is functionality issues, therefore function verification tools delivered by EDA needs to be always of high quality.

A major problem faced by the Functional verification tool R&D team is to predict the numbers of the bugs that might have been introduced during the design phase to sign off the completeness and quality. If these bugs can be predicted, then the COQ can be reduced. Hence saving million of dollar to company and customer.

Machine learning, a upcoming new discipline, define scientific study of algorithm and using computing power develop prediction model so that certainty of the task can be managed. In this project, prediction model for expected bugs during the development of the software is designed to help the Product manager to get confidence on quality. For the data, explanatory research and Interview was conducted with-in the Synopsys. This project has been successfully adopted with-in the Verification IP group of EDA leader and is in process to get it implemented in all different Business Units.