minimum cost test case generation method based on an EFSM model

Abstract

The invention relates to a minimum cost test case generation method based on an EFSM model. Based on an expansibility finite state machine and a set division theory, a migration coverage algorithm isdesigned, and a migration sequence set is obtained; And abstract models are given to different positions of the two migration sequences, analysis is carried out according to the abstract models, and then a reduction merging algorithm is designed to obtain a minimum test sequence set. Converting the minimum test sequence set to obtain an effective test path set, and finding out a shortest path froman initial state to a certain state by adopting reverse thinking in a conversion process; According to the method, the migration coverage criterion is met, invalid test paths and combination explosion problems can be avoided, the code coverage rate can be ensured, the number of the test paths in the set reaches the minimum, and the balance between the test cost and the test efficiency is ensured;The method is not only limited to an EFSM model, but also applicable to other models capable of being abstracted into directed graphs, and particularly remarkable in effect of solving a large complexnetwork graph.

Description

technical field [0001] The invention mainly relates to the field of software testing, in particular to a minimum-cost test case set generation method based on an extended finite state machine (EFSM) model. Background technique [0002] Software testing is an important means to discover errors in software and ensure software quality. At present, software testing is still a very expensive work in practical application, and it occupies a large part of the software development cost. Traditional testers manually design and run test cases, and check whether the output results are consistent with the expected results also need to be completed manually. With the continuous expansion of software scale and open source software code, software with hundreds of thousands and millions of lines of code appears more and more frequently. Relying on manual testing is not only extremely inefficient, but also costly and difficult to guarantee the quality of testing. Therefore, automation Soft...

AI Technical Summary

Problems solved by technology

[0004] However, these heuristic and search algorithms still have certain limitations. For example, the test model converted from a million-level code software or tens of millions of test data is complex and huge. Using heuristic algorithms and search algorithms to converge and query speed Slow, resulting in inefficiency in obtaining the optimal solution, and the final test case set is small, but not necessarily the smallest

When these technologies are applied to software testing based on large and complex models, not only the test cost is high, the test efficiency is low, and good results cannot be guaranteed.

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