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Lucas Bang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers
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3

FormaliSE Conference 2023 Conference Paper

Formalizing Path Explosion for Recursive Functions via Asymptotic Path Complexity

  • Eli Pregerson
  • Shaheen Cullen-Baratloo
  • David Chen
  • Duy Lam
  • Max Szostak
  • Lucas Bang

Path coverage is of critical importance in software testing and verification. Furthermore, path explosion is a well-known challenge for automatic software analysis techniques like symbolic execution. Asymptotic Path Complexity (APC) is a code complexity metric that formalizes the quantitative measurement of path explosion and therefore measures the difficulty of achieving path coverage. Prior APC methods were not sufficiently expressive to model the path explosion behavior in recursive functions. Existing analyses simply ignored recursive calls and were unable to correctly compute APC for recursive functions. We present a novel method for computing asymptotic path complexity for recursive functions, which we call APC-R. Our approach is based on the analytic combinatorics of context-free grammars. We implemented our approach on top of the code complexity analysis tool METRINOME and evaluated our implementation on a set of benchmark programs written in the C programming language. Our experiments demonstrate that APC-R is a sound upper bound on the growth rate of the number of program paths explored as a function of increasing exploration depth when generating tests with the KLEE symbolic execution engine. Our APC-R implementation provides sound bounds on KLEE’S symbolic execution path explosion for recursive functions in cases where the original APC implementation did not, and APC-R matches the results of APC for non-recursive functions with only marginal computational overhead. That is, APC-R subsumes earlier APC work without significant performance cost.

GandALF Workshop 2020 Workshop Paper

Symbolic Execution + Model Counting + Entropy Maximization = Automatic Search Synthesis

  • Mara Downing
  • Abtin Molavi
  • Lucas Bang

We present a method of automatically synthesizing steps to solve search problems. Given a specification of a search problem, our approach uses symbolic execution to analyze the specification in order to extract a set of constraints which model the problem. These constraints are used in a process called model counting, which is leveraged to compute probability distributions relating search steps to predicates about an unknown target. The probability distribution functions determine an information gain objective function based on Shannon entropy, which, when maximized, yields the next optimal step of the search. We prove that our algorithm converges to a correct solution, and discuss computational complexity issues. We implemented a domain specific language in which to write search problem specifications, enabling our static analysis phase. Our experiments demonstrate the effectiveness of our approach on a set of search problem case studies inspired by the domains of software security, computational geometry, AI for games, and user preference ranking.

TCS Journal 2015 Journal Article

R–LINE: A better randomized 2-server algorithm on the line

  • Lucas Bang
  • Wolfgang Bein
  • Lawrence L. Larmore

A randomized on-line algorithm is given for the 2-server problem on the line, with competitiveness less than 1. 901 against the oblivious adversary. This improves the previously best known competitiveness of 155 78 ≈ 1. 987 for the problem. The algorithm uses a new approach and defines a potential in terms of isolation indices from T-theory.

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