## 3 Results
\[Eff=(0.0151\cdot RC+0.3348)\times 100\%;\ R^{2}=0.8394, \tag{21}\]  
\[Eff=(-0.012\cdot RC+0.5497)\times 100\%;\ R^{2}=0.8322. \tag{22}\]  
It is important to highlight that all linear regressions conducted in this study were tailored to RC ranges from 1 to 15 for two primary reasons. Firstly, the baseline study [40] had a narrower range of 1 to 10, necessitating an adjustment of parameters to facilitate a more accurate comparison. Secondly, the focal point of our current research lies in assessing the aggressiveness of initial RC range gains. Therefore, the extrapolation employed in these current experiments aimed to provide a comprehensive view of the models' performance in higher-cost scenarios. The plots and descriptions of those models are illustrated in Figure 4. Since both models reached \(R^{2}>80\%\), they can be considered suitable for explaining efficiency and efficacy variances by RC changes. Comparing the effect sizes of RC on efficiency (\(-0.0120\)) and efficacy (\(0.0151\)), each decrease in test efficiency (caused by the increase of the RC) results in an average rise of test efficacy that is 25.8% higher than the efficiency decrease. Thus, each RC unit increment returns an improvement in test efficacy higher by almost 26% (on average) than the price paid in test efficacy decrease. Since the reduction in efficiency  
Figure 4: [Eff, Ef, RTS, EffR, and EfR] \(\times\) RC. is less than the improvement in efficacy, the same advantage observed in the benchmark study was demonstrated in the present study. Those results indicate that conveniently adjusting RC makes finding an optimal equilibrium between efficiency and efficacy and the extent of testing coverage possible. This is the core idea of the approach, which has been demonstrated only for a single project dataset until now. Thus, it indicates that the approach's core idea can be generalized for a larger dataset encompassing multiple projects developed in distinct moments by different teams involving distinct technologies (programming languages). Consequently, by adjusting the RC, test managers can use ML models to optimize the test scope according to the resources available for the software testing effort. Using ML models with lower RC will help prioritize a narrower scope of testing while maintaining high efficiency, which is advisable in scenarios where testing resources are constrained. On the other hand, in scenarios where available resources are less constrained, using higher RC values will help to expand the testing scope wisely, aiming for an improvement in efficacy despite a potential reduction in efficiency. Additionally, Table 3 presents comparisons of efficiency and efficacy with other benchmarks. One of the benchmarks used was the efficiency and efficacy of the unitary cost model (\(RC=1\)). Thus, for each RC value, the table shows how the ML model's performance (efficiency and efficacy) compares to the baseline model's performance (\(RC=1\)). Table 3 also shows the RPC, supporting a comparison between the number of modules correctly classified by each model obtained for RC \(>1\) and the baseline (\(RC=1\)). Chart (a) in Figure 5 shows the plot of the relative efficiency and efficacy, as well as the RPC, considering the unitary cost as a baseline. The same analysis performed for the absolute values of efficiency and efficacy was performed here to compare quantitatively the ratio of the decrease in relative efficiency with the increase in efficacy as RC is increased using a linear model. The expressions of regressions are shown in Equations (23) and (24),  
\[REffU=(0.058\cdot RC+1.2875)\times 100\%;R^{2}=0.8394, \tag{23}\]  
\[REfU=(-0.0196\cdot RC+0.9012)\times 100\%;R^{2}=0.8322. \tag{24}\]  
The plots and descriptions of those models are illustrated in Figure 5. Since all the regressions reached \(R^{2}>80\%\), they can be considered suitable for explaining relative efficiency and relative efficacy variances by RC changes.