Approach Overview

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Paper PDF @GPCE’16 conference: click here

The intensive use of generative programming techniques provides an elegant engineering solution to deal with the heterogeneity of platforms or technological stacks. The use of Domain Specifics Language, for example, leads to the creation of numerous code generators that automatically translate highlevel system specifications into multi-target executable code. Producing correct and efficient code generator is complex and error-prone. Although software designers provide generally high-level test suites to verify the functional outcome of generated code, it remains challenging and tedious to verify the behavior of produced code in terms of non-functional properties.

This work describes a practical approach based on a runtime monitoring infrastructure to automatically check potential inefficient code generators. This infrastructure, based on system containers as execution platforms, allows code-generator developers to evaluate the generated code performance.We evaluate our approach by analyzing the performance of Haxe, a popular high-level programming language that involves a set of cross-platform code generators that target different platforms. Experimental results show that our approach is able to detect some performance inconsistencies among Haxe generated code that reveal real issues in Haxe code generators.

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Figure 1 – A technical overview of the different processes involved to ensure the code generation and non-functional testing of produced code from design time to run time

To assess the performance/non-functional properties of generated code many system configurations (i.e., execution environments) must be considered. Running different applications (i.e., generated code) with different configurations on one single machine is complex a single system has limited resources and this can lead to performance regressions. Moreover, each execution environment comes with a collection of appropriate tools such as compilers, code generators, debuggers, profilers, etc. Therefore, we need to deploy the test harness, i. e.the produced binaries, on an elastic infrastructure that provides to compiler user facilities to ensure the deployment and monitoring of generated code in different environment settings.

Consequently, our infrastructure provides support to automatically:

  1. Deploy the generated code, its dependencies and its execution
  2. environments
  3. Execute the produced binaries in an isolated environment
  4. Monitor the execution
  5. Gather performance metrics (CPU, Memory, etc.)

To get these four main steps, we rely on system containers. Thus, instead of configuring all code generators under test (GUTs) within the same host machine, we wrap each GUT within a system container. Afterwards, a new instance of the container is created to enable the execution of generated code in an isolated and configured environment. Meanwhile, we start our runtime testing components. A monitoring component collects usage statistics of all running containers and save them at runtime in a time series database component. Thus, we can compare later informations about the resource usage of generated programs and detect inconsistencies within code generators (See Figure 1).

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Figure 2 – Setting up infrastructure for testing HAXE code generators

To assess our approach, we configure our previously proposed container-based infrastructure in order to run experiments on the Haxe case study. Figure 2 shows a big picture of the testing and monitoring infrastructure considered in these experiments.

First, we create a new Docker image in where we install the Haxe code generators and compilers (through the configuration file ”Dockerfile”). Then a new instance of that image is created. It takes as an input the Haxe library we would to test and the list of test suites (step 1). It produces as an output the source code and binaries that have to be executed. These files are saved in a shared repository. In Docker environment, this repository is called ”data volume”. A data volume is a specially-designated directory within containers that shares data with the host machine. So, when we execute the generated test suites, we provide a shared volume with the host machine so that, binaries can be executed in the execution container (Step 2). In fact, for the code execution we created, as well, a new Docker image in where we install all execution tools and environments such as php interpreter, NodeJS, etc. In the meantime, while running test suites inside the container, we collect runtime resource usage data using cAdvisor (step 3). The cAdvisor Docker image does not need any configuration on the host machine. We have just to run it on our host machine. It will then have access to resource usage and performance characteristics of all running containers. This image uses the Cgroups mechanism described previously to collect, aggregate, process, and export ephemeral real-time information about running containers. Then, it reports all statistics via web UI to view live resource consumption of each container. cAdvisor has been widely used in different projects

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