# On Fuzzy and Case-Based Dynamic Software Development Process Modeling and Simulation Approach
## 6 Discussion  
In this paper, a fuzzy and case-based dynamic SDP modeling and simulation approach has been developed and simulated to validate its performance. The advantages and uniqueness of the proposed approach are as follows: (1) it allows dynamic and parallel SDP modeling and simulation by applying a case-handling approach; (2) an ANFIS-based model is established to predict the influence of the HFs on the SDP; and (3) the simulation is implemented through the CMMN model.  
Based on the related works, the main uncertainties affecting the dynamism of BPs are the following: the behavior of process resources; changing conditions; and the sequence of tasks. First, the behavior of process resources mainly concerns the HFs, the competencies of resources, and psychological aspects. Therefore, we added an HF prediction method based on ANFIS to predict the effect of the HFs on the SDP more accurately. Second, changing conditions are related to the SDP context and application domain. As the SDP is dynamic, changing conditions can impact the specifics of each software product. Therefore, additional components for describing and managing a context and application domain are necessary for SDP simulation. Consequently, the changing conditions of the SDP are modeled by applying CMMN. Finally, the sequence of tasks is one of the uncertainties affecting the dynamism of BPs, as it can change depending on the scope of the SDP and the state of the software product. In summary, the proposed fuzzy and case-based dynamic SDP modeling and simulation approach has advantages and contributes to SDP modeling and simulation by considering the HFs, process dynamism, and changing contexts. All of these factors allow for SDP modeling and simulation that is more in line with reality.  
In terms of design, the contribution of this research lies in successfully implementing and demonstrating the proposed fuzzy and case-based dynamic SDP modeling and simula  
Figure 11: Comparison of possible deviations in actual execution time with and without the HF during simulation.  
tion approach in a prototype and conducting experiments with it. Two types of experiment were conducted as follows: (1) without including the HF in SDP modeling and simulation and (2) with the inclusion of the HF in SDP modeling and simulation. Finally, the results of both experiments were compared using statistical tests and the minimum and maximum deviation of estimated task execution time.  
The obtained results show that the inclusion of the HFs in SDP modeling and simulation may lead to different results in predicting the execution time of SDP tasks than we would achieve without the inclusion of the HFs. The most significant possible differences between the results of both experiments were obtained when the person is entirely unmotivated (i.e., the HF is low, in which case the SDP execution time deviation is the largest). Consequently, including the HFs in SDP modeling and simulation can lead to the more realistic prediction of SDP task execution time.  
Another advantage of the proposed approach is that it can be adapted to different SDP methodologies by adjusting the proposed SDP CMMN model. Moreover, other HFs can be used for SDP modeling and simulation, such as by changing the composition of the HF itself and increasing or decreasing the sensitivity of the model to the HF by increasing or decreasing the number of linguistic terms.  
This research also shows the suitability of ANFIS for HF prediction in the software product development area. The accuracy of the statistical tests (MSE = 0.196872, RMSE = 0.038716) obtained in this research shows the well-fitting predictions provided by the trained ANFIS model.