THE INTELLIGENCE SYSTEM OF SOFTWARE COMPLEXITY AND QUALITY EVALUATION AND PREDICTION Oksana Pomorova, Tetyana Hovorushchenko Khmelnitsky National University

Safety Case Methodology The main task of Safety Case methodology is the automating of the creation of: software requirements profile (including standards for software development, subject domain standards and customer requirements); software analysis results profile - metric analysis results, source code and software test results; evaluation of results profile accordance to requirements profile. Our task - automating of the metric analysis results processing.

Unsolved Tasks of Metric Analysis Results Processing: absence of unified standards for metrics, which leads to subjective selection of quality evaluation methods; difficulty of interpretation the metrics values, which is caused by individual projects features and absence of metrics standard values; absence of criterion to compared essentially new and previous projects, which leads to the impossibility of interpretation of obtained metrics for new project; basic parameters in the selection of software realization versions are the design cost and time and software company reputation, but the decisions, taken on the basis of these parameters, not guarantee software quality.

On the basis of the above the need and actuality of scientific research in development of new effective methods of software quality evaluation and prediction arises. The intelligent methods, in particular artificial neural net's method of software quality evaluation and prediction, are perspective today.

Metrics of Software Design Stage

The Structure of Intelligence System of Software Complexity and Quality Evaluation and Prediction (ISCQEP) ISCQEP Structure

ISCQEP Components The dialog (interface) module visualizes the functioning of module of data collection and communication, displays the system functioning and produces the messages to user in an understandable form for him. The module of data collection and communication reads the user information about the quantitative values of exact and predicted metrics of software design stage, saves the obtained information in the knowledge base and transmits its to the module of ANN input vectors forming. Knowledge base contains the quantitative values of exact and predicted metrics of software design stage, the ANN input vectors and the rules of ANN results processing. The module of ANN input vectors forming prepares the metrics values of the knowledge base for the ANN inputs.

The artificial neural network provides the approximation of software design stage metrics and gives the quantitative evaluation of project complexity and quality and prediction of designed software complexity and quality characteristics. Input data for ANN are the set of the design stage metrics with the exact values and the set of the design stage metrics with the predicted values. If a certain metric was not determined, the proper element of set will be equal -1. Multilayer perceptron is ANN for solving of task of the metrics analysis and software quality characteristics prediction. This ANN has 24 neurons of the input layer, 14 neurons of approximating layer and 8 neurons of the adjusting layer and 4 neurons of the output layer. Realized neural network was trained with training sample of 1935 vectors and tested with testing sample of 324 vectors by one step secant backpropagation method (OSS). The training performance is 0,

ANN architecture in Simulink Structural scheme of ANN layers

Structural scheme of ANN 1-st layer Structural scheme of ANN 2-nd layer Structural scheme of ANN 3-rd layer Structural scheme of ANN 4-th layer

ANN evaluations: –project complexity estimate; –project quality evaluation; –software complexity prediction; –software quality prediction are values in the range [0, 1], where 0 - proper metrics were not determined, approximately 0 - the project or designed software has a high complexity or low quality and 1 - the project or software is simple or high quality. The module of ANN results processing makes the conclusions about the project quality and complexity and the expected quality and complexity of designed software on the basis of an analysis of 4-th obtained results.

Processing of Stage Design Metrics Using ISCQEP The project has low complexity and high quality The designed software will has low complexity and high quality The project has significant complexity and low quality The designed software will has significant complexity and low quality The project has medium complexity and medium quality The designed software will has medium complexity and quality The project has low complexity and high quality The designed software will has low complexity and high quality The project has significant complexity and low quality The designed software will has significant complexity and low quality

On the basis of ANN results, design cost and time the choice of project version was performed. Both versions have approximately the same design cost and time, but significantly different estimates of project complexity and quality and prediction of designed software complexity and quality. On the basis of only cost and time software company can make a false conclusion about selection of the project version. ANN evaluations help to make the right selection.

ACKNOWLEDGMENT The necessity and actuality of scientific research in software quality evaluation and prediction comes from the results of the software metric evaluation methods analysis. The main parameters in the selection of software project version are the design cost and time and designing company reputation, but a decisions on the basis of these parameters are not always guarantee the proper software quality.

Predicted evaluations of designed software complexity and quality give the prediction about complexity and quality of concrete project version realization and allow to compare the different project versions, when the cost and time is approximately equal. The proposed intelligence system of software complexity and quality evaluation and prediction provides the motivated and grounded decision about selection of the project version on the basis not only cost and time, but also considering quality characteristics.

Problems: metric information lack to increasing of the training and testing samples size; need the such diverse utilities to comparing of metric information processing results of this project; need the development of designed software complexity evaluation metrics from the viewpoint of the maintenance difficulty or simplicity, usability and the effectiveness of the methods chosen to solve the task.

References Bishop P. A Methodology for Safety Case Development / P. Bishop Kelly T. Arguing Safety – A Systematic Approach to Managing Safety Cases / T. Kelly A. Gordeyev, V. Kharchenko, A. Andrashov, B. Konorev, V. Sklyar, A. Boyarchuk. Case-Based Software Reliability Assessment by Fault Injection Unified Procedures // Proceedings of the 2008 International Workshop on Software Engineering in East and South Europe – Germany, Leipzig, – pp. 1-8 Pomorova O.V., Hovorushchenko T.O. Analysis of Methods and Tools of Software Systems Quality Evaluation // Radioelectronic and Computer Systems – Kharkiv: KhAI, 2009 – N6, pp Pomorova O.V., Hovorushchenko T.O. Intelligence Method of Design Results Evaluation and Software Quality Characteristics Prediction // Radioelectronic and Computer Systems – Kharkiv: KhAI, 2010 – N6, pp Pomorova O.V., Hovorushchenko T.O., Tarasek S.Y. Analysis and Processing of Software Quality Metrics on the Design Stage // Transactions of Khmelnitsky National University – Khmelnitsky: KhNU, N1, pp.54-63

Our Contacts 29016, Ukraine, Khmelnitsky, Institutska str., 11 Khmelnitsky National University Department of system programming Oksana Pomorova Doctor of Technical Sciences, Professor, Head of System Programming Department Tetyana Hovorushchenko Ph.D., Senior Researcher, Associate Professor, Lecturer of System Programming Department