AI Summary & Key Takeaways
AI InsightAccording to the results obtained, the correct determination rate of sum of the metrics method, which was developed to identify the classes that should primarily be tested, is ranged between 55% and 68%.
📌 3 Key Takeaways
Abstract
Early detection and correction of errors appearing in software projects reduces the risk of exceeding the estimated time and cost. An efficient and effective test plan should be implemented to detect potential errors as early as possible. In the earlier phases, codes can be analyzed by efficiently employing software metric and insight can be gained about error susceptibility and measures can be taken if necessary. It is possible to classify software metric according to the time of collecting data, information used in the measurement, type and interval of the data generated. Considering software metric depending on the type and interval of the data generated, object-oriented software metric is widely used in the literature. There are three main metric sets used for software projects that are developed as object-oriented. These are Chidamber & Kemerer, MOOD and QMOOD metric sets. In this study, an approach for identifying the classes that should primarily be tested has been developed by using the object-oriented software metric. Then, this approach is applied for selected versions of the project developed. According to the results obtained, the correct determination rate of sum of the metrics method, which was developed to identify the classes that should primarily be tested, is ranged between 55% and 68%. In the random selection method, which was used to make comparisons, the correct determination rate for identifying the classes that should primarily be tested is ranged between 9.23% and 11.05%. In the results obtained using sum of the metrics method, a significant rate of improvement is observed compared to the random selection method.