Termination Analysis Without the Tears
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Abstract (arXiv)
Determining whether a given program terminates is the quintessential undecidable problem. Algorithms for termination analysis are divided into two groups: (1) algorithms with strong behavioral guarantees that work in limited circumstances (e.g., complete synthesis of linear ranking functions for polyhedral loops [Podelski and Rybalchenko, 2004]), and (2) algorithms that are widely applicable, but have weak behavioral guarantees (e.g., Terminator [Cook et al., 2006]). This paper investigates the space in between: how can we design practical termination analyzers with useful behavioral guarantees? This paper presents a termination analysis that is both compositional (the result of analyzing a composite program is a function of the analysis results of its components) and monotone ("more information into the analysis yields more information out"). The paper has two key contributions. The first is an extension of Tarjan's method for solving path problems in graphs to solve infinite path problems. This provides a foundation upon which to build compositional termination analyses. The second is a collection of monotone conditional termination analyses based on this framework. We demonstrate that our tool ComPACT (Compositional and Predictable Analysis for Conditional Termination) is competitive with state-of-the-art termination tools while providing stronger behavioral guarantees.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2021.
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Citing papers per year: 2021: 4; 2022: 4; 2023: 1.
9 papers.
- 2021Neural Termination Analysiscited by 16
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- 2021Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generationcited by 0
- 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectorscited by 0
- 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networkscited by 0
- 2022Explanation-Guided Fairness Testing through Genetic Algorithmcited by 0
- 2022Using Graph Neural Networks for Program Terminationcited by 0
- 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGramcited by 0
- 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGram16 Mar 2023
- 2022Using Graph Neural Networks for Program Termination28 Jul 2022
- 2022Explanation-Guided Fairness Testing through Genetic Algorithm16 May 2022
- 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks8 Mar 2022
- 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors6 Feb 2022
- 2021Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation13 Sep 2021
- 2021VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search5 Aug 2021
- 2021SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis3 Aug 2021
- 2021Neural Termination Analysis7 Feb 2021