Software system research and development quality evaluation method and system, storage medium and electronic equipment

Abstract

The invention discloses a software system research and development quality evaluation method and system, a storage medium and electronic equipment. The evaluation method comprises a data collection step of collecting a historical research and development dimension index group and a historical quality score corresponding to the historical research and development dimension index group, a predictionmodel obtaining step of training an initial model through the historical research and development dimension index group and the historical quality score to obtain a prediction model, and an evaluation step of inputting the latest research and development dimension index group into the prediction model to carry out software research and development quality evaluation to obtain a latest quality score. Based on the method, software test engineers and managers do not need to manually evaluate each dimension of the software project quality each time, and only need to input objective dimension indexes into the system to obtain a quality score result.

Description

technical field [0001] The present invention relates to the field of software system research and development, in particular, to a method, system, storage medium and electronic equipment for evaluating the quality of software system research and development based on machine learning. Background technique [0002] With the continuous development of the Internet, machine learning and artificial intelligence have gradually replaced human decision-making in various fields. Leaders in all walks of life are trying to discover new business growth points and monetization channels. [0003] In this context, for the software R&D process, there are relatively many opportunities and scenarios to use machine learning in the design of requirements and software development, such as generating various algorithm models and predicting various business data, etc., but in software In the testing process, there are quite few application scenarios for machine learning. Software testing itself ha...

AI Technical Summary

Problems solved by technology

This method has the following defects: the dimensions of collected information cannot be standardized, and usually rely on the subjective judgment of testers; there is no common standard in the industry for the way of analyzing data, and different teams usually design a set of systems or formulas to evaluate The quality of software development is not universal

[0005] Specifically, there is currently no general software development quality evaluation system or tool in the industry, and each project team uses evaluation dimensions, formulas or self-developed systems suitable for the project itself to evaluate software development quality

At the same time, there is no standardized input dimension indicator and standardized evaluation formula. All evaluation indicators are determined by manual subjective decision-making, and different projects need to adjust the dimensions and evaluation formulas. The credibility of the evaluation results is not high.

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