Pith. Sign in

RECORD

Tighter Loop Bound Analysis (Technical report)

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Pavel Čadek, Jan Strejček, Marek Trtík

Programming Languages · Submitted 11 May 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results show that the algorithm implemented in a prototype tool Looperman often produces tighter bounds than current tools for loop bound analysis.

how it is stated · asserted 0.89 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID1605.03636 (version 1)
Pith IDOEZOZZQP, Pith's permanent record of this paper and its review
CitationJan Strej\v{c}ek, Marek Trt\'ik, Pavel \v{C}adek. "Tighter Loop Bound Analysis (Technical report)." arXiv:1605.03636 (2016). https://pith.science/paper/OEZOZZQP
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,123 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.10 · human participants 0.02 · controlled comparison 0.56 · randomized assignment 0.01 · states quantitative result 0.14 · compares with prior results 0.92 · mentions code or data availability 0.17.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We present a new algorithm for computing upper bounds on the number of executions of each program instruction during any single program run. The upper bounds are expressed as functions of program input values. The algorithm is primarily designed to produce bounds that are relatively tight, i.e. not unnecessarily blown up. The upper bounds for instructions allow us to infer loop bounds, i.e.~upper bounds on the number of loop iterations. Experimental results show that the algorithm implemented in a prototype tool Looperman often produces tighter bounds than current tools for loop bound analysis.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present a new algorithm for computing upper bounds on the number of executions of each program instruction during any single program run. contribution 1.00 0.95 0.19 asserted 1.00
s1 The upper bounds are expressed as functions of program input values. contribution 0.53 0.77 0.05 asserted 1.00
s2 The algorithm is primarily designed to produce bounds that are relatively tight, i.e. not unnecessarily blown up. contribution 0.75 0.48 0.12 asserted 0.98
s3 The upper bounds for instructions allow us to infer loop bounds, i.e.~upper bounds on the number of loop iterations. interpretation 0.79 0.64 0.11 asserted 0.98
s4 Experimental results show that the algorithm implemented in a prototype tool Looperman often produces tighter bounds than current tools for loop bound analysis. result 1.00 0.96 0.58 asserted 0.89

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.96.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present a new algorithm for computing upper bounds on the number of executions of each program instruction during any single program run. The upper bounds are expressed as functions of program input values. The algorithm is primarily designed to produce bounds that are relatively tight, i.e. not unnecessarily blown up. The upper bounds for instructions allow us to infer loop bounds, i.e.~upper bounds on the number of loop iterations. Experimental results show that the algorithm implemented in a prototype tool Looperman often produces tighter bounds than current tools for loop bound analysis.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.