Early Software Reliability Prediction Using Cause-effect Graphing Analysis

Early prediction of software reliability can help organizations make informed decisions about corrective actions. To provide such early prediction, we propose practical methods to: 1) systematically identify defects in a software requirements specification document using a technique derived from cause-effect graphing analysis (CEGA); 2) assess the impact of these defects on software reliability using a recursive algorithm based on binary decision diagram (BDD) technique. Using a numerical example, we show how predicting software reliability at the requirement analysis stage could be greatly facilitated by the use of the method presented in this paper. The acronyms used throughout this paper are alphabetically listed as follows: ACEG-actually implemented cause effect graph; BCEG-benchmark cause effect graph; BDD-binary decision diagram; CEGA-cause effect graphing analysis; PACS-personal access control system; SRS-software requirements specification document