dependability-toolbox-usage.md

@@ -33,13 +33,11 @@ where the <local_IP> is the IP of the local machine on which the Dependability T
 - **Vulnerability Prediction**: /VulnerabilityPrediction
 - **Optimum Checkpoint Interval Recommendation:** /OptimalCheckpoints
 
-In the following, a more detailed description of how these services can be used is provided. Please use the following links to navigae more easily within the page:
+In the following, a more detailed description of how these services can be used is provided. Please use the following links to navigate more easily within the page:
 
-- [Quantitative Security Assessment]()
-- [Vulnerability Prediction]()
-- [Optimum Checkpoint Interval Recommendation]()
+[[_TOC_]]
 
-### Quantitative Security Assessment Service
+### Quantitative Security Assessment Service {: #qsa}
 
 The Quantitative Security Assessment web service allows the user to evaluate the internal security level of software applications written in Java, C, and C++ programming languages. This is achieved through a dedicated API exposed by the RESTful web server, which is, in fact, a simple HTTP GET request. Several inputs need to be provided as parameters to this request. These parameters are listed below: 
 
@@ -286,7 +284,7 @@ As can be seen from the fragment above, the JSON report consists of four main el
 
 - **security_index:** An object that contains the Security Index of the analyzed software. 
 
-### Vulnerability Prediction
+### Vulnerability Prediction {: #vpm}
 
 The *Vulnerability Prediction* service is responsible for identifying security hotspots, i.e., source code files that are likely to contain vulnerabilities. It is based on Deep Learning Models that receive as input the tokens (i.e., keywords) retrieved directly from the source code of the selected software project (written either in Java, C, or C++ programming language) and provide an indication of how likely its software files contain vulnerabilities. This is achieved through a dedicated API exposed by the RESTful web server, which is, in fact, a simple HTTP GET request. Several inputs need to be provided as parameters to this request. These parameters are listed below: 
 
@@ -354,7 +352,7 @@ As can be seen from the above fragment, the JSON report comprises an array named
 
 - **sigmoid:** The actual output of the multi-layer perceptron. It corresponds to the probability of the source code file to be vulnerable. This value is actually used by the model in order to define the vulnerability status of the corresponding source code file (i.e., the value of the “is_vulnerable” entry of the corresponding JSON Object). 
 
-## Optimum Checkpoint Interval Recommendation
+## Optimum Checkpoint Interval Recommendation {: #oci}
 
 This web service is responsible for calculating the checkpoint interval of long loops, i.e., the checkpoint interval that strikes a satisfactory balance between reliability, performance, and energy consumption. It is actually an impelemntation of the novel mathematical model that was introduced as part of the SDK4ED Project.  The calculation of the Optimum Checkpoint intervals is achieved through a dedicated API exposed by the RESTful web server, which is, in fact, a simple HTTP POST request. Several inputs need to be provided as parameters to this request. These parameters are listed below: