The paper develops the method for forecasting the level of software quality based on quality attributes. This method differs from the known ones in that it provides forecasting the quality level of future software based on the processing the software quality attributes’ values, which are available in the software requirements specification (SRS). So, the proposed method makes it possible to compare the SRSs, to immediately refuse the realization of a software based on unsuccessful SRS (saving money and time, reducing the probability of failed and challenged projects), and to make a reasonable choice of the specification for the further implementation of a software with the highest quality (of course, if errors will not be introduced at subsequent stages of the software life cycle). During the experiments, 4 SRS were analyzed, which were fulfilled by different IT firms of Khmelnytskyi (Ukraine) for the solution of the same task. Taking into account the forecasted quality level of the future software, which will have developed according to each of the analyzed SRS, a comparison of the 4 analyzed SRS was made, and a reasoned choice of the specification was made for the further realization of the highest quality software.
The paper proposes a neural-network model of software quality prediction based on quality attributes. The proposedmodel differs from the known models, because it provides considering the importance of each quality attribute and their interactionwithin each software quality characteristic. The artificial neural network (ANN) outputs correspond to the values of software qualitycharacteristics (functional suitability, performance efficiency, usability, reliability, compatibility, security, maintainabi lity, portability).The artificial neural network (ANN) outputs make it possible assessing the total impact of quality attributes on software qualitycharacteristics
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