**4. Results**

This research utilizes the grounded theory to prove the correction rate of the XREAP tool. The success of the XREAP approach can be indirectly proven by the comparison results of traditional method and the XREAP tool. The XREAP tool is a method for requirements elici‐ tation and analysis. Alternatively, it can be adopted to list the problem variables, extract the implicit problems, and analyze the at-hand solutions.

The more association lines among actors and use cases, the more complex relationship with the requirements of the specific problem-solving. For example, a use case diagram with twenty association lines among its actors and use cases is absolutely complex than the other use case diagram with only five association lines.

As the use case diagram shown in Figure 4, the decision-makers can count on the num‐ bers of the association lines among actors and use cases. That is, there are seven use cas‐ es and six actors that are associated with eleven directed association lines and five <<include>> dependency lines, one <<extend>> association line, and three generalization relationship linesfor implementing a virtual suicide prevention gatekeeper, Socio-Health, in the Facebook environment. Note that this case study only covers the adolescents and young adults in Taiwan.

The statistical table of shape items is also shown in Table 1 and the final score of the com‐ plexity calculation of the Socio-Health problem is 58. Note that the shape item of the use case is categorized as three levels: generic use case(s), included use case(s), and extended use case(s). A generic use case can include and/or extend one more use case. Therefore, the generic use case might own higher complexity weight than the included and extended use case(s). Based on our implementation experiences, the complexity of most included use cas‐ es is higher than the one of most extended use cases. Similarly, the shape item of the actor is also categorized into six levels: related to one use case, related to 2~4 use cases, related to 5~8 use cases, related to at least nine use cases, and generalized. The corresponding weights are assigned by their implementation complexities.

Table 2 shows the problem complexity assessment range for the analyst to estimate the final calculation of the XREAP tool. Based on the Table 2, the complexity score is below 100 is categorized as tiny problem and correspondingly easy to handle.

Based on complexity assessment for such a use case diagram, we can decide to execute these implementation tasks. Correspondingly, the generic decision-making by intuition for the same task might be also similar to the result for utilizing the XREAP tool and consider this



**Table 1.** Statistical table of shape items for utilizing XREAP tool


**Table 2.** Problem complexity assessment range

#### **5. Discussion**

specific sources of the requirements' illustration and then further transform a new use case diagram to replace the original diagram. Although such a modification procedure of the XREAP tool is not so convenience, anyhow, it urges the analysts to reconsider and confirm

This research utilizes the grounded theory to prove the correction rate of the XREAP tool. The success of the XREAP approach can be indirectly proven by the comparison results of traditional method and the XREAP tool. The XREAP tool is a method for requirements elici‐ tation and analysis. Alternatively, it can be adopted to list the problem variables, extract the

The more association lines among actors and use cases, the more complex relationship with the requirements of the specific problem-solving. For example, a use case diagram with twenty association lines among its actors and use cases is absolutely complex than the other

As the use case diagram shown in Figure 4, the decision-makers can count on the num‐ bers of the association lines among actors and use cases. That is, there are seven use cas‐ es and six actors that are associated with eleven directed association lines and five <<include>> dependency lines, one <<extend>> association line, and three generalization relationship linesfor implementing a virtual suicide prevention gatekeeper, Socio-Health, in the Facebook environment. Note that this case study only covers the adolescents and

The statistical table of shape items is also shown in Table 1 and the final score of the com‐ plexity calculation of the Socio-Health problem is 58. Note that the shape item of the use case is categorized as three levels: generic use case(s), included use case(s), and extended use case(s). A generic use case can include and/or extend one more use case. Therefore, the generic use case might own higher complexity weight than the included and extended use case(s). Based on our implementation experiences, the complexity of most included use cas‐ es is higher than the one of most extended use cases. Similarly, the shape item of the actor is also categorized into six levels: related to one use case, related to 2~4 use cases, related to 5~8 use cases, related to at least nine use cases, and generalized. The corresponding weights

Table 2 shows the problem complexity assessment range for the analyst to estimate the final calculation of the XREAP tool. Based on the Table 2, the complexity score is below 100 is

Based on complexity assessment for such a use case diagram, we can decide to execute these implementation tasks. Correspondingly, the generic decision-making by intuition for the same task might be also similar to the result for utilizing the XREAP tool and consider this

their requirements carefully, not unceremoniously.

implicit problems, and analyze the at-hand solutions.

use case diagram with only five association lines.

are assigned by their implementation complexities.

categorized as tiny problem and correspondingly easy to handle.

young adults in Taiwan.

**4. Results**

10 Decision Support Systems

Based on our empirical outcomes, the following arguments will focus on five significant concerns: limitation of the XREAP tool, the ratio of requirements elicitation, divide-and-con‐ quer, complexity assessment, and decision-making guidelines.

#### **5.1. Limitation of the XREAP tool**

As the utilization of the XREAP tool to make some decisions for several projects, we found some pros and cons. They are listed in Table 3 for the analyst further reference. Further‐ more, the XREAP tool owns some limitations. For example, the mind brainstorm function supports graphical user interface for user requirements by categories. That is, every PMI item can provide a number of the entries. However, the arrangement of the requirements' map is not so concise that some of the requirements might be overlapped each other, and the screen will be too small to browse while every PMI item is more than 15 entries.

until they can cope with the scope of the problem. The divide-and-conquer methodology is widely used in several fields such as computer science. Similarly, the decision-makers are problem-solvers. Therefore, decision-makers can try to analyze the small problems one by

Whether Moving Suicide Prevention Toward Social Networking: A Decision Support Process with XREAP Tool

http://dx.doi.org/10. 5772/51985

13

Generally speaking, the complexity assessment is not an easy task. As our proposed meth‐ odology illustration, the complexity can be counted for the numbers of the actors and use cases in the final use case diagram. The more actors and use cases, the more complex inter‐ woven network for requirements will be presented. Although the roughly count of the use cases and actors might be too simple to convey the complexity of the requirements, such a computation method is easy for decision-makers to confirm the existing input requirements quickly and repeatedly. However, it is possible for researchers to propose better complexity assessment for the XREAP tool in the future. Based on the complexity assessment results,

Although the XREAP tool is one of the simple software for eliciting requirements, it can be‐ come a supplement to improve the decision-making quality for decision-makers. Normally, it is necessary for decision-makers to refer the decision-making guidelines that are gathered by other decision-makers. As the popularity of the Internet, it is possible for decision-mak‐ ers to share and revise their decision-making guidelines in the cloud. Based on the knowl‐ edge management experiences from the healthcare field in 2008 [4] , it is feasible to share, revise and manage the specific knowledge through the network. That is, if the decision-mak‐ ing guidelines are utilized and revised by most decision-makers, then the optimal decision-

It is a smart behaviour for decision-makers to spend more time to realize the whole views of the problems and solutions before they make wise decisions. However, an effective decision analysis tool is hard to obtain. The XREAP software is an optional choice for assisting deci‐ sion-makers. As the tool results said, the SP service can be spread through SN, and it ex‐

The authors would like to thank all research colleagues in the National Suicide Prevention Centre, Taipei, Taiwan. The authorsalso express thanks for partial financial support from

plores and assists the potential subjects who present the trend of suicide ideation.

the National Science Council, Taiwan, under grant number NSC101-2220-E017-001.

one and integrate all solutions into a total solution for original problem.

decision-makers can conveniently make their decision.

**5.4. Complexity assessment**

**5.5. Decision-making guidelines**

making process will be generated.

**6. Conclusion**

**Acknowledgements**


**Table 3.** Pros and cons of the XREAP tool

#### **5.2. The ratio of requirements elicitation**

Fundamentally, the requirements elicitation is the first phase in our decision-making proc‐ ess. As most of the decision-makers known, the higher ratio of requirements elicitation is ob‐ tained, the better quality of decision-making will be executed. If decision-makers are eager for the highest quality of their decision-making, it is necessary for them to try to focus on the requirements elicitation phase. Fortunately, our proposed methodology can elicit required information from users by utilizing the XREAP tool. Meanwhile, the implicit information for persons, actions, tenancies, environment and equipment can be elicited by the XREAP tool as possible as it could extract from user requirements by both PMI and APC methods. Fur‐ thermore, all requirements are listed within a tabular frame in the XREAP tool, and it is con‐ venient for the decision-makers to browse and review. As compared with other decisionmaking tools, we believe the XREAP tool can supply the exhaustive capability to elicit user requirements.

#### **5.3. Divide-and-conquer**

If the problem is too large to solve, it is feasible for problem-solvers to utilize the divideand-conquer approach to decompose the problem into several smaller problems. If the smaller problem is still too large to handle, problem-solverscan divide such a problem again until they can cope with the scope of the problem. The divide-and-conquer methodology is widely used in several fields such as computer science. Similarly, the decision-makers are problem-solvers. Therefore, decision-makers can try to analyze the small problems one by one and integrate all solutions into a total solution for original problem.

#### **5.4. Complexity assessment**

**5.1. Limitation of the XREAP tool**

12 Decision Support Systems

Pros Cons

Can exchange use case diagram with the XML metadata

interchange standard

requirements.

**5.3. Divide-and-conquer**

**Table 3.** Pros and cons of the XREAP tool

**5.2. The ratio of requirements elicitation**

As the utilization of the XREAP tool to make some decisions for several projects, we found some pros and cons. They are listed in Table 3 for the analyst further reference. Further‐ more, the XREAP tool owns some limitations. For example, the mind brainstorm function supports graphical user interface for user requirements by categories. That is, every PMI item can provide a number of the entries. However, the arrangement of the requirements' map is not so concise that some of the requirements might be overlapped each other, and

the screen will be too small to browse while every PMI item is more than 15 entries.

Is cross-platform Is a bit slow during execution

Supports mind brainstorm function Is not easy for utilization

Is visualization Is not beautiful on graphic user interface

Can transfer from requirements to a use case diagram Cannot reverse transfer from a use case diagram to

Can be utilized as a decision support tool Does not yet include the calculation function of the

Fundamentally, the requirements elicitation is the first phase in our decision-making proc‐ ess. As most of the decision-makers known, the higher ratio of requirements elicitation is ob‐ tained, the better quality of decision-making will be executed. If decision-makers are eager for the highest quality of their decision-making, it is necessary for them to try to focus on the requirements elicitation phase. Fortunately, our proposed methodology can elicit required information from users by utilizing the XREAP tool. Meanwhile, the implicit information for persons, actions, tenancies, environment and equipment can be elicited by the XREAP tool as possible as it could extract from user requirements by both PMI and APC methods. Fur‐ thermore, all requirements are listed within a tabular frame in the XREAP tool, and it is con‐ venient for the decision-makers to browse and review. As compared with other decisionmaking tools, we believe the XREAP tool can supply the exhaustive capability to elicit user

If the problem is too large to solve, it is feasible for problem-solvers to utilize the divideand-conquer approach to decompose the problem into several smaller problems. If the smaller problem is still too large to handle, problem-solverscan divide such a problem again

requirements

complexity assessment

Can only exchange with the Star UML tool

Generally speaking, the complexity assessment is not an easy task. As our proposed meth‐ odology illustration, the complexity can be counted for the numbers of the actors and use cases in the final use case diagram. The more actors and use cases, the more complex inter‐ woven network for requirements will be presented. Although the roughly count of the use cases and actors might be too simple to convey the complexity of the requirements, such a computation method is easy for decision-makers to confirm the existing input requirements quickly and repeatedly. However, it is possible for researchers to propose better complexity assessment for the XREAP tool in the future. Based on the complexity assessment results, decision-makers can conveniently make their decision.

#### **5.5. Decision-making guidelines**

Although the XREAP tool is one of the simple software for eliciting requirements, it can be‐ come a supplement to improve the decision-making quality for decision-makers. Normally, it is necessary for decision-makers to refer the decision-making guidelines that are gathered by other decision-makers. As the popularity of the Internet, it is possible for decision-mak‐ ers to share and revise their decision-making guidelines in the cloud. Based on the knowl‐ edge management experiences from the healthcare field in 2008 [4] , it is feasible to share, revise and manage the specific knowledge through the network. That is, if the decision-mak‐ ing guidelines are utilized and revised by most decision-makers, then the optimal decisionmaking process will be generated.
