IPSynth: Interprocedural Program Synthesis for Software Security Implementation

• URL: http://arxiv.org/abs/2403.10836v1
• Date: Sat, 16 Mar 2024 07:12:24 GMT
• Title: IPSynth: Interprocedural Program Synthesis for Software Security Implementation
• Authors: Ali Shokri, Ibrahim Jameel Mujhid, Mehdi Mirakhorli,
• Abstract summary: We introduce IP Synth, a novel inter-procedural program synthesis approach that automatically learns the specification of the tactic. Our results show that our approach can accurately locate corresponding spots in the program, synthesize needed code snippets, add them to the program, and outperform ChatGPT in inter-procedural tactic synthesis tasks.
• Score: 3.1119394814248253
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To implement important quality attributes of software such as architectural security tactics, developers incorporate API of software frameworks, as building blocks, to avoid re-inventing the wheel and improve their productivity. However, this is a challenging and error-prone task, especially for novice programmers. Despite the advances in the field of API-based program synthesis, the state-of-the-art suffers from a twofold shortcoming when it comes to architectural tactic implementation tasks. First, the specification of the desired tactic must be explicitly expressed, which is out of the knowledge of such programmers. Second, these approaches synthesize a block of code and leave the task of breaking it down into smaller pieces, adding each piece to the proper location in the code, and establishing correct dependencies between each piece and its surrounding environment as well as the other pieces, to the programmer. To mitigate these challenges, we introduce IPSynth, a novel inter-procedural program synthesis approach that automatically learns the specification of the tactic, synthesizes the tactic as inter-related code snippets, and adds them to an existing code base. We extend our first-place award-winning extended abstract recognized at the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE'21) research competition track. In this paper, we provide the details of the approach, present the results of the experimental evaluation of IPSynth, and analyses and insights for a more comprehensive exploration of the research topic. Moreover, we compare the results of our approach to one of the most powerful code generator tools, ChatGPT. Our results show that our approach can accurately locate corresponding spots in the program, synthesize needed code snippets, add them to the program, and outperform ChatGPT in inter-procedural tactic synthesis tasks.

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