Recently, a variety of studies have been conducted on source code analysis. If auto-generated code is included in the target source code, it is usually removed in a preprocessing phase because the presence of auto-gen...
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ISBN:
(纸本)9781450357142
Recently, a variety of studies have been conducted on source code analysis. If auto-generated code is included in the target source code, it is usually removed in a preprocessing phase because the presence of auto-generated code may have negative effects on source code analysis. A straightforward way to remove autogeneratedcode is searching special comments that are included in the files of auto-generated code. However, it becomes impossible to identify auto-generated code with the way if such special comments have disappeared for some reasons. It is obvious that it takes too much effort to see source files one by one manually. In this paper, we propose a new technique to identify auto-generated code by using the naturalness of auto-generated code. We used a golden set that includes thousands of hand-made source files and source files generated by four kinds of compiler-compilers. Through the evaluation with the dataset, we confirmed that our technique was able to identify auto-generated code with over 99% precision and recall for all the cases.
Recently, a variety of studies have been conducted on source code analysis. If auto-generated code is included in the target source code, it is usually removed in a preprocessing phase because the presence of auto-gen...
详细信息
ISBN:
(数字)9781450357142
ISBN:
(纸本)9781538661697
Recently, a variety of studies have been conducted on source code analysis. If auto-generated code is included in the target source code, it is usually removed in a preprocessing phase because the presence of auto-generated code may have negative effects on source code analysis. A straightforward way to remove auto-generated code is searching special comments that are included in the files of auto-generated code. However, it becomes impossible to identify auto-generated code with the way if such special comments have disappeared for some reasons. It is obvious that it takes too much effort to see source files one by one manually. In this paper, we propose a new technique to identify auto-generated code by using the naturalness of auto-generated code. We used a golden set that includes thousands of hand-made source files and source files generated by four kinds of compiler-compilers. Through the evaluation with the dataset, we confirmed that our technique was able to identify auto-generated code with over 99% precision and recall for all the cases.
Recently, many researchers have conducted mining source code repositories to retrieve useful information about software development. Source code repositories often include auto-generated code, and auto-generated code ...
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ISBN:
(纸本)9781509018512
Recently, many researchers have conducted mining source code repositories to retrieve useful information about software development. Source code repositories often include auto-generated code, and auto-generated code is usually removed in a preprocessing phase because the presence of auto-generated code is harmful to source code analysis. A usual way to remove auto-generated code is searching particular comments which exist among auto-generated code. However, we cannot identify autogeneratedcodeautomatically with such a way if comments have disappeared. In addition, it takes too much time to identify autogeneratedcode manually. Therefore, we propose a technique to identify auto-generated codeautomatically by using machine learning techniques. In our proposed technique, we can identify whether source code is auto-generated code or not by utilizing syntactic information of source code. In order to evaluate the proposed technique, we conducted experiments on source codegenerated by four kinds of code generators. As a result, we confirmed that the proposed technique was able to identify autogeneratedcode with high accuracy.
Embedded automotive software is currently showing trends towards model predictive control (MPC), virtual sensors or model-based diagnosis, mainly used in advanced driver assistance systems (ADAS) and automated driving...
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ISBN:
(纸本)9781728151380
Embedded automotive software is currently showing trends towards model predictive control (MPC), virtual sensors or model-based diagnosis, mainly used in advanced driver assistance systems (ADAS) and automated driving. Such applications use physical models in the control algorithms. The integration of physical models is a risky task, since weaknesses, such as the need for floating-point arithmetic and discretization or model properties, such as discontinuities and nonlinearities, quickly bring a project to a standstill or establish errors in the final product. The use of known verification and validation methods is often not possible or offers false safety guarantees. This article is intended to help developers understand and identify safety weaknesses and develop new verification and validation methods specifically adapted for physics-based, critical, embedded code. For this purpose, corresponding weaknesses in current industrial projects with physics-based systems have been identified and categorized. In this article, these are described and illustrated with examples from applications in order to get an idea of their relevance in the current context. On this basis, approaches for the analysis and diagnosis of potentially faulty code are proposed to motivate testers and quality managers to find new methods for error identification and validation of critical, physics-based, embedded code.
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