Sistemas de reuso cl´ asico aplicados a las operaciones

The first question was about the effect of declining the Responsiveness parameter on the QoE value. The results are shown in Figure 7-6, which shows that 39.7% of the participants were satisfied with this scenario. Equivalent numeric values were fed to the fuzzy engine to estimate the QoE value; the result was 3.525 that is equal to good; a similar result has been achieved in the user study.

Figure 7-6 participants’ results for Responsiveness parameter

The second question was about the effect of declining the Features parameter on the QoE value. The results are shown in Figure 7-7, with 43.8% of the participants choosing

136

neutral. Equivalent numeric values were fed to the fuzzy engine to estimate the QoE value, the result of the fuzzy engine was 4.52 that is equal to excellent; while the result obtained in the user study was fair.

Figure 7-7 participants’ results for Features parameter

The third question investigated diminishing the Security parameter on the QoE value. The results are illustrated in Figure 7-8 with 41.1% of the participants strongly dissatisfied with the scenario. The results obtained from inputting similar data to the fuzzy engine reported that the QoE value was 3.525 which is equivalent to the good term, but the result obtained in the questionnaire was bad. The results achieved from the survey reflects the high effect of this parameter on user satisfaction.

137

The influence of declining the Rapport parameter on the QoE value was studied in the fourth question. The results are as shown in Figure 7-9, with 42.5% of the participants had been satisfied. On the other hand, the results obtained from running the fuzzy engine with the same input data produced a QoE value of 4.52 which is excellent. The results obtained in the survey were good.

Figure 7-9 participants’ results for Rapport parameter

Exploring the effect of dropping the Flexibility parameter on the QoE value was considered in the fifth question. The results are presented in Figure 7-10, with 37% of the participants being satisfied with this scenario. Nevertheless, the results acquired by computing the QoE value using the fuzzy engine was 4.514 which is excellent; the results obtained in the survey were good.

138

The sixth question was about the impact of declining the Reliability on the QoE value. The results are shown in Figure 7-11, with 52.1% of the participants were dissatisfied with this scenario. Simulating the fuzzy engine with similar input data resulted in QoE value equal to 4.52 which is excellent. However, the result obtained in the survey was poor, which is an indicator of the high impact of this factor on QoE.

Figure 7-11 participants’ results for Reliability parameter

The seventh question considered a combination of the parameters values on the QoE value. The input values of the parameters Responsiveness, Reliability, Flexibility, Security, Features, and Rapport were set to bad, good, medium, medium, medium, and medium respectively. The results are depicted in Figure 7-12, with 37% of the participants were dissatisfied. While the results acquired by simulating the fuzzy engine with this set of data resulted in a QoE value of 2.525 which is fair. On the other hand, the results obtained in the survey were poor.

139

The eighth question introduced another combination of the parameters values to find their effect on QoE. The values of Responsiveness, Reliability, Flexibility, Security, Features, and Rapport were considered good, medium, good, good, good, and bad respectively. The results are shown in Figure 7-13, with 43.8% of the participants were dissatisfied. While the results of the fuzzy engine were 4.508 which is excellent, the result obtained in the survey was poor.

Figure 7-13 participants’ results for the eighth question

The ninth question studied the effect of considering the values of Responsiveness, Reliability, Flexibility, Security, Features, and Rapport parameters to good, good, medium, good, bad, and bad respectively. The results are presented in Figure 7-14, with 37% of the participants were dissatisfied. While the results obtained from the fuzzy engine showed that QoE was 2.525 which is good, the result obtained in the survey was poor.

140

The tenth question considered a combination of the parameter values on the QoE value. The parameters were selected as: Responsiveness: medium, Reliability: medium, Flexibility: good, Security: bad, Features: good, and Rapport: good]. Figure 7-15 shows the results of the study, with 52.1% of the participants dissatisfied. Whilst the results obtained from the fuzzy simulation was 3.525 which is good, the result obtained in the survey was bad.

Figure 7-15 participants’ results for the tenth question

The overall obtained results are summarized in Table 7-4, which shows a comparison between the results achieved by the fuzzy engine and the results obtained by the user study. This table gives a good indication of the difference between the expected results and the realistic data.

141

Table 7-4 Comparison between the results of Fuzzy decision and the survey study

Ca

se

Input Data Fuzzy Output Study Output

Responsiveness linguistic Responsiveness numeric Reliability linguistic Reliability numeric Flexibility linguistic Flexibility numeric Security linguistic Security numeric Features linguistic Features numeric Rapport linguistic Rapport numeric Fuzzy results linguistic Fuzzy results numeric

Study results Study results

1 Bad 30 Good 88 Good 90 Good 95 Good 93 Good 99 Good 3.525 Satisfied Good

2 Good 98 Good 92 Good 89 Good 90 Bad 40 Good 94 Excellent 4.52 Neutral Fair

3 Good 99 Good 99 Good 92 Bad 22 Good 90 Good 91 Good 3.525 Strongly

dissatisfied Bad

4 Good 90 Good 96 Good 100 Good 89 Good 95 Bad 34 Excellent 4.52 Satisfied Good

5 Good 100 Good 90 Bad 15 Good 99 Good 88 Good 95 Excellent 4.514 Satisfied Good

6 Good 95 Bad 8 Good 88 Good 100 Good 97 Good 90 Excellent 4.52 Dissatisfied Poor

7 Medium 73 Bad 20 Medium 75 Medium 70 Medium 73 Good 94 Fair 2.525 Dissatisfied Poor

8 Good 98 Good 89 Good 95 Good 90 Bad 22 Medium 74 Excellent 4.508 Dissatisfied Poor

9 Bad 50 Good 91 Medium 73 Good 95 Bad 18 Good 100 Fair 2.525 Dissatisfied Poor

10 Good 92 Medium 74 Good 93 Bad 21 Good 89 Medium 75 Good 3.525 Strongly

dissatisfied Bad

142