Our approach is based on a novel software network representation of software structure (i.e., class coupling network) and a simulation algorithm for change propagation. A novel metric, (software stability), is developed to measure software stability. References [15–17, 24] measured design stability (or class stability) by characterizing the difference between two successive versions.
You have to choose between the object store database and relational database, two solid data storage that has their tradeoffs. However, scaling to meet the demand of consumers is still a challenging process. Building scalable software means you have to be more meticulous with the development details. This property does not hold for any OO metric, since the alteration of the order of the statements does not affect the CCN and its corresponding . There is only one parameter (i.e., ) that should be set before executing Algorithm 1.
Concurrent Users
Other columns contain the key classes identified by each approach. We use “” to signify the corresponding class is identified by each approach. http://ul-gsm.ru/elcreade760-2.html The last four rows of each table show the metric data for each approach. Table 4 shows the that we compute for each pair of software systems.
In this section, we perform experiments to examine whether converges to a relative stable value. CCN is a weighted directed graph that represents the coupling relations between classes/interfaces in a specific software system. Specifically, all classes/interfaces in the software are modeled as graph nodes while the coupling relations are modeled as weighted directed links between nodes. For example, if class ’s methods call methods of class , there exists a direct link () in the graph, where is the caller class, and is the callee class. Each link is assigned a weight to denote the probability that a change in one class (the tail end of the link) will propagate to another class (the head end of the link) connecting to it. In this work, we do not differentiate classes and interfaces, treating them as the same thing.
One Performance Testing Scenario is Enough.
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Suppose is the of any node in (i.e., the randomly selected initial node is fixed to node ). Is defined as the set of nodes in CCN that are affected by changes in one randomly selected node in the -th simulation. Thus, the number of changed nodes in the -th simulation is denoted as . There might be many “circular couplings” existing in CCN, which will affect the change propagation process. As shown in Figure 3, “V1” depends on “V2”, and “V2” in turn depends on “V1”. Thus, in a particular propagation process, changes in “V1” can propagate to “V2”, and the change in “V2” will go back to “V1”.
Map and Monitor Your Network
Table 2 gives some simple descriptions of the selected subject systems, including their names and versions, the domain they belong to, the directory we analyze, and URLs to download these systems. We also show some size-related metrics, i.e., thousand lines of code (KLOC), number of packages (#P), number of classes (#C), number of methods (#M), and number of attributes (#A). These values are computed from the Java code in the directory listed in the “Directory” column, not the whole distribution of the corresponding software. The reliability test definition is an activity that determines if there are data leaks (stability testing) and how much time is needed for the system to recover after a failure (recovery testing). Beyond that, it also analyzes the behavior under peak loads and during (stress/spike testing) an emulated component failure (failover testing). The goal of reliability testing is to improve the mean time between failure (MTBF), mean time to failure (MTTF), and mean time to repair (MTTR) and offer a set of improvement guidelines for the development team.
- Table 8 shows the CPU time required for each step when applied to subject software systems listed in Table 2.
- It’s important to test these procedures before a time
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- They usually assumed changes in one class definitely affect other classes linking to it directly and indirectly.
- The theoretical results show that our metric satisfies most of Weyuker’s properties only with two exceptions, and the empirical results show that is an effective indicator for software quality improvement and class importance.
It can be observed from Table 6 that, when applied to ant, obtains a recall of 80%, a precision of 5.90%, an F1 of 10.99%, and an F5 of 53.94%, all better than the other four approaches in the baseline. It indicates that 80% of the key classes in the ground truth of ant can be retrieved by . Similarly, we can observe from Table 7 that, when applied to JMeter, obtains a recall of 28.57%, a precision of 10.33%, an F1 of 15.17%, and an F5 of 26.75%, all better than the other four approaches in the baseline.