Method for assisting the verification and operational validation of an algorithm chain, said method comprising a sequence of steps comprising at least: a) a step of instrumenting the algorithm chain, automatically capturing data that include at least some of the following data: interface data and internal algorithm data of the algorithm chain, step a) consisting in reading the backup files in which is stored a data set of the algorithm chain and creating queries about the data contained in said backup files; b) a step of automatically verifying functional rules, step b) being configured to be able to detect a breach of the rules and to be able to detect if elements have to be verified by an operator; c) a learning step for modelling by learning the constraints and algorithm criteria of the algorithm chain, taking into account validations carried out by a human operator, when operational validation is carried out, and data captured, the learning using means based on one of the following features: - support vector machines; - neural networks; - decision trees; - statistical methods; and - linear discriminant analysis; and d) a step of automatically detecting bugs or functional problems in the algorithm chain using statistical learning means, step d) carrying out a correlation of the data that include at least data of the algorithm chain, using statistical learning means, in order to determine general rules of variations in algorithm outputs of the algorithm chain, and sending a warning if a variation greater than a given threshold of an algorithm output compared with a predetermined output is detected. a) a step of instrumenting the algorithm chain, automatically capturing data that include at least some of the following data: interface data and internal algorithm data of the algorithm chain, step a) consisting in reading the backup files in which is stored a data set of the algorithm chain and creating queries about the data contained in said backup files; b) a step of automatically verifying functional rules, step b) being configured to be able to detect a breach of the rules and to be able to detect if elements have to be verified by an operator; c) a learning step for modelling by learning the constraints and algorithm criteria of the algorithm chain, taking into account validations carried out by a human operator, when operational validation is carried out, and data captured, the learning using means based on one of the following features: - support vector machines; - neural networks; - decision trees; - statistical methods; and - linear discriminant analysis; and - support vector machines; - neural networks; - decision trees; - statistical methods; and - linear discriminant analysis; and d) a step of automatically detecting bugs or functional problems in the algorithm chain using statistical learning means, step d) carrying out a correlation of the data that include at least data of the algorithm chain, using statistical learning means, in order to determine general rules of variations in algorithm outputs of the algorithm chain, and sending a warning if a variation greater than a given threshold of an algorithm output compared with a predetermined output is detected.