Models for Testing Modifiable Systems

Abstract

The work describes reliability and security growth models for modifiable software systems as a result of revisions and tests performed for specified input data areas. The work shows that the known reliability growth models are of monotonically increasing type, which is not in line with current multi-version team technologies of software development that are primarily based on the open-source code. The authors suggest new nonmonotonically increasing models of software reliability evaluation and planning that allow taking into account the effect of decreased reliability resulting from updates or wavefront errors. The work describes the elaborated bigeminal and generic reliability evaluation model as well as the models and test planning procedures. The work includes calculated expressions for the evaluation of the model accuracy and shows that the developed models are adequate to real data. An example is given of transition from probability models to fuzzy models in case of incomplete basic data. The work provides general recommendations for selection of software tool testing models.

Simulation of Software Security Tests by Soft Computational Methods

“…There are a few taxonomies of mathematical software reliability models known at present [2,[19][20][21][22]. We believe that, from the applied perspective, it is convenient to use the classification relating to the objectives of software product/system testing stages [21], which allows 4 types of mathematical models to be singled out: [3,[21][22][23][24][25][26] which imply a strictly monotonous growth of software reliability. At present, this is not always consistent with current multiversion programming technologies.…”

“…At present, this is not always consistent with current multiversion programming technologies. To rule out the above drawback, the authors gave a thorough study to software implementation of probabilistic reliability growth model allowing some additional positive and negative debugging factors to be taken into account, namely [21]:…”

“…As far as the updating of a probabilistic model"s parameters (1) is concerned, the most graphical model is the bigeminal debugging one taking account of opposite software modification classes, namely: correcting an error and introducing a new software functional [21].…”

“…The bigeminal model (2) depends on 4 parameters ( 0 , ∞ , 1 , 2 ) easy to calculate, for example, using the maximum likelihood method [21]. No restrictions are imposed on the domain of defining the modification scope factor to allow consistency and completeness of describing the testing and debugging process.…”

“…Showing the confidence level allows the desired interval fuzzy model to be obtained in general terms: This approach, including membership function obtaining techniques, is described in detail in [21].…”