The process of software testing is a crucial phase in determining the quality of software, and this phase requires significant costs and a considerable amount of time for testers. This paper discusses the development of a framework for software quality assessment, involving flexible choices of software testing methods and variables in the form of an application. The method used is experimental, developing a new framework based on previous research, where previous research was limited to specific methods and testing variables. The result of this research is the creation of a new framework for software quality assessment. It is hoped that this framework can serve as a reference for software companies in evaluating software quality. In terms of complexity, this framework has the advantage of allowing a tester to choose methods with more flexible or unlimited testing variables. Regarding the estimated time and costs, with [Formula: see text], the practical application complexity of the developed framework is estimated to have the best costs, time and human resources at IDR 254,240,000, with an estimated time of 3,178 work hours and 6,356 work hours with a team of 3 people.

In software development, the software testing phase is an important process in determining the quality level of the software. Software testing is a process of executing a program aimed at finding errors in module access, units, and involves the execution of the system being tested on a number of test inputs, and determining whether the output produced is correct. In this study, a model-based testing (MBT) called integration-based model (I-BM) framework will be developed. This I-BM framework integrates testing variables from several software testing methods, namely black-box testing, white-box testing, unit testing, system testing, and acceptance testing. The integrated variables are function, interface, structure, performance, requirement, documentation, positives, and negatives. Then, this framework will document software errors to form a dataset, which will be measured for the level of accuracy of expected manual fault output using neural network algorithm and support vector machine. From the experiment results, it shows that the accuracy level of predicting fault output values from the I-BM framework using the neural network algorithm is on average 80%, and it produces a superior SVM architecture model in predicting I-BM framework output errors with an accuracy value of 0.99, precision of 0.99, recall of 0.99, and [Formula: see text]-score of 0.99. Compared to other MBT, the IBM framework has the advantage of being a more comprehensive software testing model because it starts from the identification of problems, analysis, design, documentation of software testing, and recommendations for each fault output found. Thus, software errors can be classified systematically in the form of a dataset, and not only focus on software testing for product lines and module mappings.