Abstract: Because the existing algorithms fail to incorporate the principle of minimum confidence and big data technology in the static analysis process, the static analysis results are inaccurate and costly. A static analysis algorithm for embedded software maturity based on minimum confidence and big data is proposed. The minimum confidence is used as the subordination criterion. The static analysis ideas of embedded software maturity are evaluated by weight and satisfaction, and evaluation indicators are constructed. Big data technology is used to standardize and non-dimensionalize various software maturity evaluation indicators to obtain effective indicators. Combining the evaluation dimensions of embedded software maturity, using attribute mathematics principles, the evaluation index measurement is calculated and analyzed in the form of expert questionnaires, a static evaluation model of embedded software maturity is built, and the static analysis of software maturity is completed through the model. The simulation experiment results show that the proposed algorithm can obtain high-accuracy static analysis results of embedded software maturity, and the static analysis cost can also be effectively reduced.

Key words: minimum confidence, big data, embedded software maturity, static analysis algorithm