In the result it can be observed that the anti_pattern was detected and highlighted in general software system work scheme.

3.2 Chatty Service

Chatty Service corresponds to a set of services that exchange a lot of small data of primitive types, usually with a Data Service antipattern. The Chatty Service is also characterized by a high number of method invocations. Chatty Service chats a lot with each other.

RULE CARD: ChattyService {

RULE: ChattyService { INTER TotalInvocation DSRuleCard };

RULE: DSRuleCard { RULE CARD: DataService };

RULE: TotalInvocation { NMI VERY HIGH };

Here the NMI is “number of method invocations” metric.

The execution trace containing interconnection chain that represents “Chatty Service” anti_pattern is presented in table 4.

Sample event log for “Chatty Service” anti_pattern.

As it can be observed from the event log, two of three services communicate a lot with each other. They are Srv1 and Srv2. As we do not have enough information to estimate amount of transferred data during calls, we rely only on behavioral part of the rule, i.e. communicating part.

The result of work of anti_pattern detection algorithm is presented on fig. 3.2.

Figure 1.12. Result demonstration of detection of “Chatty Service” anti_pattern.

In the result it can be observed that the anti_pattern was detected and highlighted in general software system work scheme.

Conclusion

This work addresses the issue of necessity of monitoring circumstance of software systems implemented through service-based principle in conditions of continuous development and enhancement when number and complexity of systems is expanding faster than a human being can handle.

In the context of this work classification of antipatterns was provided and overview of methods to detect these antipatterns were observed. Methods to detect attribute specific and relational specific antipatterns already exist. And class of metric specific antipatterns was chosen for further detailed investigation.

During the exploration of process mining and data mining domains the general service-specific antipattern detection rules were invented. All rules consist of several metrics and its specific values, describing symptoms of antipatterns. At the time, five metrics are available for usage in detection rules: incoming call rate, outcoming call rate, response time, cohesion with other services, and number of service connections. With applying these metrics several antipatterns was specified and algorithms for it detection were introduced.

Algorithms of antipattern detection based on metric calculation were implemented as a software tool (research prototype), which allows by specifying rule cards in XML format and log in SQL Server database to detect antipatterns. The software tool is developed with usage of ASP.NET MVC framework.

Evaluation was conducted on logs from real service-based software system. In the process of evaluation several anti_patterns were found and it was confirmed by developer team that almost 80% of them were really presented.

The necessity of dynamic analysis was one of results of research work. And in accordance to that new functionality on dynamic data analysis was added to the developed research prototype. For now a few metrics can be calculated taking into time changes and it is planned to support all calculating now metrics from the dynamic point of view. It is planned that in future the work on refinement of the information system will be continued according to analysis of real life logs and number of available metrics and possible to detect antipatterns will be significantly greater. Furthermore, some fuzziness can be introduced for the evaluation of the threshold values thus to make anti_pattern detection rules more flexible.

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