AN EMPIRICAL-STUDY OF TESTING AND INTEGRATION STRATEGIES USING ARTIFICIAL SOFTWARE SYSTEMS

There has been much discussion about the merits of various testing and integration strategies. Top-down, bottom-up, big-bang, and sandwich integration strategies are advocated by various authors. Also, some authors insist that modules be unit tested, while others believe that unit testing diverts resources from more effective verification processes. This article addresses the ability of the aforementioned integration strategies to detect defects, and produce reliable systems. It also explores the efficacy of spot unit testing, and compares phased and incremental versions of top-down and bottom-up integration strategies. Relatively large artificial software systems were constructed using a code generator with ten basic module templates. These systems were seeded with known defects and tested using the above testing and integration strategies. A number of experiments were then conducted using a simulator whose validity was established by comparing results against these artificial systems. The defect detection ability and resulting system reliability were measured for each strategy. Results indicated that top-down integration strategies are generally most effective in terms of defect correction. Top-down and big-bang strategies produced the most reliable systems. Results favored neither those strategies that incorporate spot unit testing nor those that do not; also, results favored neither phased nor incremental strategies.