## 6 Experimentation Results and Discussion
### Cost-Benefit Analysis for Realistic Applications of the Proposed Approach  
SQA is a process that runs concurrently with software development. It aims to improve the software-development process so that issues can be avoided before they become a big problem. SQA is a form of umbrella operation that covers the entire software-development process. Testing is the process of using every function of a product in order to ensure that it meets quality-control requirements. This might include putting the product to use, stress testing it, or checking to see if the actual service results match the predicted ones. This method detects any flaws before a product or service goes live [40]. Programming testing is a fundamental activity, and it is over-the-top expensive to test the software defects altogether. An investigation on the Cost of Software Quality (CoSQ) model announced that the expense of software defects in the USA is 2.84 trillion dollars and, furthermore, influenced more than four billion individuals on the planet [42]. Thus, the early identification of programming flaws can be helpful for computer programmers to diminish the expense and time of the production of a software [26]. In Google, the half code base changes each month. Thus, Google utilizes a code-audit strategy for all new source codes to guarantee a solid code base. Nonetheless, Google engineers work on more unpredictable issues ordinarily. In this way, it is difficult to audit all new source codes; even some source codes function admirably. To determine these issues, specialists utilize defect  
Figure 2: ROC curves of NB and PL-NB, and RF and PL-RF for jm1 and KC1 datasets. forecasts to distinguish the problem areas in codes and caution the product designer to fix the deficient module [43]. A few analysts likewise recommended that field bug forecasts can diminish the dangers related to field faults for programmers and shoppers on a huge scope industry such as ABB Inc. [44]. It is critical for a software manager to know whether they can depend on a bug prediction model as a wrong prediction of the number or the location of future bugs can lead to problems [10]. Concept drift is inherent in software-defect datasets, and its existence thereafter degrades the performance of prediction models [13]. The concept drift should be considered by software quality-assurance teams while building prediction models. Existing techniques, such as predicting co-evolution based on historical data, are weakened by the concept drift effect as software evolves [45]. [PERSON] et al. [46] studied software co-evolution  
Figure 3: ROC curves of NB and PL-NB, and RF and PL-RF for prop defect datasets. based on both component dependency and historical data in order to discover other methods that can overcome the weakness of existing techniques. Recently, [PERSON] et al. [47] proposed a concept drift-aware temporal cloud service API recommendation approach for composite cloud systems to adapt user's preference drifts and the provide effective recommendations to composite cloud system developers. Additionally, [PERSON] and [PERSON] [48] investigated distributed machine learning-based ensemble techniques to detect the presence of concept drift in network traffic and to detect network-based attacks. Cost-Benefit Analysis Results  
The normalized cost values (NormCost) of the basic and proposed approach are presented in Table 7. The NormCost value from the respective PL for each dataset is reported in the table, and values less than 1.0 indicate that the suggested approach is cost-effective. This means that, if SDP results are combined with CD adaptation, overall testing costs and time can be reduced. Values greater than 1.0, on the other hand, indicate that CD adaptation in SDP in that scenario is ineffective at reducing testing costs and effort; therefore, it is recommended that CD adaptation in SDP models be avoided in such cases. In contrast, the value of 1 for NormCost indicates that the PL-based technique could not reduce the testing costs and efforts.