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REVIEW 3 major objections 31 references

LLM-generated programs, especially when paired across languages or with human-written code, can improve reliability in 1-out-of-2 systems by reducing overlapping failures.

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2026-07-12 04:20 UTC pith:H25NJFBP

load-bearing objection First solid EL/LM-style measurement of failure diversity in large LLM code pools, with real (spec-dependent) 1-out-of-2 gains and occasional negative human–LLM correlation; external validity is the only real soft spot. the 3 major comments →

arxiv 2607.03174 v1 pith:H25NJFBP submitted 2026-07-03 cs.SE cs.AI

Effectiveness of LLM-based Software Diversity for Reliability Improvement -- an Empirical Study

Gabriel Almeida , Ilir Gashi , Vladimir Stankovic , Jo\~ao R. Campos This is my paper
classification cs.SE cs.AI
keywords software reliabilitysoftware diversityN-version programminglarge language modelscode generationfailure correlationdiverse redundancyempirical evaluation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper asks whether large language models can serve as practical, low-cost generators of software diversity, and how much reliability that diversity can actually buy. Traditional multi-version programming is expensive because humans must independently implement, validate, and maintain several versions of the same specification; LLMs can emit large pools of candidates quickly across models, temperatures, and languages. The authors extend classical reliability analyses of human-written contest programs to matching pools of LLM-generated code for three specifications, measuring 1-out-of-2 reliability improvement under common tests. They find that LLM programs still suffer correlated failures, but that heterogeneous pairings—different languages, or one human and one LLM program—often yield larger gains than homogeneous pools, and in places even outperform independent-failure expectations. A sympathetic reader cares because this points to a scalable way to obtain the diversity that fault-tolerant design has long wanted but could rarely afford.

Core claim

Combining LLM-generated programs, especially in heterogeneous settings across programming languages and across LLM-generated and human-written programs, can yield reliability gains in a 1-out-of-2 configuration. The gains depend on the specification, language, and generation setting; LLMs do not remove correlated failures, but they supply a scalable source of comparatively low-cost programs whose diversity can be leveraged for reliability improvement. In some pools, enforced human–LLM pairs show negative failure correlation and outperform pure human, pure LLM, and mixed homogeneous pools.

What carries the argument

The reliability improvement ratio R (average single-program probability of failure on demand divided by the average PFD of 1-out-of-2 pairs), evaluated on failure vectors under the Eckhardt–Lee homogeneous model and the Littlewood–Miller heterogeneous/forced-diversity model as pools are filtered by reliability threshold.

Load-bearing premise

That failure diversity measured on three competitive-programming problems with contest-style test suites, after discarding totally incorrect programs, is informative about reliability improvement for software diversity in general.

What would settle it

Run the same 1-out-of-2 R analysis on larger industrial-style modules with realistic operational profiles and show that heterogeneous LLM or human–LLM pairs no longer improve reliability over homogeneous human pairs once low-reliability programs are filtered out.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • LLMs can act as practical generators of multi-version candidates whose diversity is usable for reliability improvement at far lower cost than human-only N-version programming.
  • Enforcing structural heterogeneity (different languages, or human versus LLM) yields larger reliability gains than drawing pairs at random from a single mixed pool.
  • Prompting for different programming languages is a strong catalyst for failure diversity, often outperforming temperature or model-family variation alone within one language.
  • Fault-tolerant designs should preserve meaningful source heterogeneity between redundant components rather than simply pooling all available programs.
  • Higher generation temperature increases diversity within the LLM pool but does not change the fundamental gap between human and LLM failure profiles.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the pattern holds beyond contest tasks, hybrid human–LLM multi-version designs could become a default option for modules where full formal verification is impractical.
  • Shared pre-training corpora may cap how much independent diversity pure multi-LLM ensembles can achieve without explicit diversity objectives in generation or training.
  • Very large R values from pairing a strong human program with a weak local model can be driven by the human version alone; designers should track absolute system PFD, not only the improvement ratio.
  • The same framework can test whether modern AI-assisted human code still retains complementary failure modes relative to pure LLM code, or whether the two sources are converging.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 0 minor

Summary. The paper extends classical software-diversity analysis (Eckhardt–Lee / Littlewood–Miller) from human-written programs to large pools of LLM-generated code. Using three UVa Online Judge specifications (3n+1, Factors and Factorials, Factovisors), it builds comparable human and LLM program pools under a shared compilation, sandboxing, timeout, and exhaustive-test pipeline, then measures reliability improvement R = PFD_A / PFD_AB in 1-out-of-2 configurations. Diversity is induced along model family, temperature, and programming language; analyses cover homogeneous pools, language-forced heterogeneous pairs, and human–LLM pairs. The main empirical claim is that LLM-generated programs can yield reliability gains, especially under heterogeneous pairing (across languages and across human vs. LLM sources), and that in some pools enforced human–LLM pairs approach or exceed the independence bound, including occasional negative failure correlation.

Significance. If the results hold under the paper’s stated scope, this is a useful bridge between classical N-version / forced-diversity empirics and modern LLM code generation. Strengths include: (i) direct application of the EL/LM reliability-improvement framework rather than only pass@k-style correctness; (ii) a large, multi-model, multi-language, multi-temperature generation design; (iii) a pre-LLM historical human corpus that provides a contamination-free baseline for cross-source comparison; and (iv) replication of known human failure-correlation patterns, which anchors the new LLM results. The work is primarily empirical measurement, not a closed-form derivation, and is valuable as a controlled reference study even if industrial transfer remains open.

major comments (3)
  1. §IV.B.2–IV.C and Figs. 1–3: Several headline R values (e.g., ~44,378× for LLM C++/Java on 3n+1; ~2.9×10^6 for Human–Local at PFD 10^{-5}; curves crossing independence) sit in regimes where the manuscript itself notes pool depletion and residual single fault types can inflate gains. For these load-bearing tail claims, report the effective number of remaining programs (and distinct score classes) at each plotted threshold, and add uncertainty (e.g., bootstrap or leave-one-score-class-out) so that “negative correlation” and order-of-magnitude leaps are not driven by a handful of residual vectors.
  2. §III.B and §IV.D.1: The 3n+1 suite uses a restricted range (i∈[1,100], j∈[1,50]) and Factovisors is “sampled from ranges matching the 3n+1 specification,” while Factors and Factorials uses the full 99-case range. Difficulty-landscape and human–LLM orthogonality conclusions in §IV.D depend on these suites. Clarify sampling procedure, justify comparability with van der Meulen & Revilla’s historical setup, and state whether re-running on fuller UVa ranges would change the Hard/Medium/Low partitions or the heterogeneous R curves.
  3. §IV.C (Human–Local vs Human–Commercial) and Discussion §V.D: The manuscript correctly flags an “improvement paradox” when a weak Local model pairs with a strong human program (very large R but system reliability dominated by one component). Make this operational in the results: alongside R, report absolute pair PFD (or equivalent) for pure-human, pure-LLM, and human–LLM pairs at matched single-program PFD, so readers can separate diversity effectiveness from baseline quality imbalance—especially where negative correlation is claimed.

Circularity Check

0 steps flagged

No significant circularity: empirical R is measured from observed failure vectors under shared oracles, not derived by construction from fitted targets or self-citation chains.

full rationale

This paper is an empirical measurement study, not a first-principles derivation that claims to predict reliability gains from axioms. The reliability improvement ratio is defined as R = PFD_A / PFD_AB from observed single-program and 1-out-of-2 pair failure rates on fixed exhaustive test suites (§III.C); the reported curves are therefore direct statistics of the failure vectors, not quantities forced by a fitted parameter renamed as a prediction. EL and LM models are used only as interpretive framing for homogeneous vs. heterogeneous pairings (§II.A, §III.F), not as equations that force the numerical gains. Human baselines are reconstructed from historical UVa submissions and are shown to reproduce known correlation patterns from prior independent work [10], [18], [19]; LLM pools are generated under controlled axes (model family, temperature, language) and evaluated under the same compilation/sandbox/oracle pipeline. Filtering (exclude PFD=1, one program per human author) is a stated methodological choice that can affect pool composition, but it does not make R tautological: R still measures residual failure overlap among retained programs. Self-citations are limited to standard background on NVP and diversity models; no uniqueness theorem or ansatz from the authors is load-bearing for the central claim. Occasional negative correlation (heterogeneous human–LLM pairs crossing independence in Fig. 3) is an observed outcome under the shared oracles, not an input. External-validity limits (contest tasks, three specifications) are scope issues, not circularity. Score 0 is therefore appropriate.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

The central claim rests on classical diversity models and experimental design choices rather than new physical entities. Load-bearing assumptions are equiprobable programs/tests for R, the 1-out-of-2 failure definition, filtering of totally incorrect programs, and the representativeness of contest specs and pre-LLM human submissions. Free parameters are operational thresholds and generation settings chosen by the experimenters, not fitted to force the reliability conclusion.

free parameters (4)
  • generation temperature grid = 0.5–1.5
    Temperatures {0.5, 0.75, 1.0, 1.25, 1.5} are experimenter-chosen decoding settings that affect validity and homogeneous diversity; not derived from theory.
  • per-test timeout = 0.2 s; 30 consecutive
    0.2 s timeout (adjusted for Java) and halt after 30 consecutive timeouts follow prior heuristics and define which programs count as failures.
  • programs requested per configuration = 100 (up to 2000 per model)
    100 programs per model/language/temperature (or 400 for APIs without temperature control) is a sampling budget choice that shapes pool composition.
  • PFD filtering threshold schedule = incremental discard of high-PFD programs
    Sliding PFD thresholds discard least-reliable programs to plot R vs baseline reliability; the schedule is an analysis design choice that concentrates perfect programs at ultra-low average PFD.
axioms (4)
  • domain assumption 1-out-of-2 system fails only if both programs fail the same test case; R = average single-program PFD / average pair PFD under equiprobable programs and tests.
    Adopted from van der Meulen & Revilla / Popov et al. as the reliability-improvement metric (§III.A,C).
  • domain assumption Eckhardt–Lee / Littlewood–Miller models: coincident failures are driven by shared test-case difficulty and covariance between development processes; independence is not assumed.
    Theoretical basis for interpreting homogeneous vs forced-diversity curves (§II.A, §III.F).
  • ad hoc to paper Excluding PFD=1 programs and retaining the first valid program per human author yields a fair diversity pool comparable to prior human studies.
    Filtering rule stated in §III.D–E; changes pool size and may bias toward more reliable partial programs.
  • domain assumption Historical UVa submissions form a contamination-free pre-LLM human baseline for cross-source comparison.
    Methodological claim in Introduction and §VI.C; assumes limited modern AI influence and limited LLM training leakage of the exact analyzed corpus.

pith-pipeline@v1.1.0-grok45 · 22106 in / 3315 out tokens · 34489 ms · 2026-07-12T04:20:18.690605+00:00 · methodology

0 comments
read the original abstract

Software diversity has been extensively studied as a means of reducing the risk of common-mode failures. Classic work showed that the central issue is whether failures of diversely redundant components overlap in ways that limit the reliability gains. Traditional software diversity is costly to obtain, since it requires multiple implementations as well as the corresponding validation, maintenance, and deployment effort. Recent advances in Large Language Models (LLMs) may change this. LLMs enable inexpensive code generation: they produce many candidate implementations of the same specification quickly, across different models, decoding settings, and programming languages. This raises a natural question: can LLMs serve as practical generators of software diversity, and how much reliability improvement can that diversity actually provide? In this paper, we extend classical empirical studies of software diversity in human-written programs to LLM-generated code. We study three specifications using both historical human-written programs and large pools of LLM-generated ones evaluated under a common compilation, sandboxing, and exhaustive test suite. We explore LLM diversity along multiple axes, including model family, generation temperature, and programming language. Reliability improvement is evaluated in a 1-out-of-2 configuration across both homogeneous and heterogeneous program populations, including within-LLM pairings and pairings across programming languages and across LLM-generated and human-written programs. The results show that combining LLM-generated programs, especially in heterogeneous settings, can yield reliability gains, although this is partly conditioned by the programming language and generation setting. Taken together, these findings suggest that LLMs provide a scalable source of comparatively low-cost programs whose diversity can be leveraged for reliability improvement.

Figures

Figures reproduced from arXiv: 2607.03174 by Gabriel Almeida, Ilir Gashi, Jo\~ao R. Campos, Vladimir Stankovic.

Figure 1
Figure 1. Figure 1: Homogeneous 1-out-of-2 reliability improvement comparing human-written and LLM-generated programs, analyzed as a unified pool of all programs [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Heterogeneous 1-out-of-2 reliability improvement across different programming languages, comparing human-written (dashed lines) and LLM-generated [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Global 1-out-of-2 reliability improvement comparing pure configurations ( [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Combined scatter plots comparing individual test case failure rates of human-written programs against Commercial and Local LLM programs. Overlaid [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

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Reference graph

Works this paper leans on

31 extracted references · 7 linked inside Pith

  1. [1]

    Basic concepts and taxonomy of dependable and secure computing,

    A. Avizienis, J.-C. Laprie, B. Randell, and C. Landwehr, “Basic concepts and taxonomy of dependable and secure computing,”IEEE transactions on dependable and secure computing, vol. 1, no. 1, pp. 11–33, 2004

    2004

  2. [2]

    The use of triple-modular redundancy to improve computer reliability,

    R. E. Lyons and W. Vanderkulk, “The use of triple-modular redundancy to improve computer reliability,”IBM journal of research and develop- ment, vol. 6, no. 2, pp. 200–209, 1962

    1962

  3. [3]

    C. R. Spitzer,Avionics: development and implementation. CRC Press, 2018

    2018

  4. [4]

    The methodology of n-version programming,

    A. Avizienis, “The methodology of n-version programming,”Software fault tolerance, vol. 3, pp. 23–46, 1995

    1995

  5. [5]

    A theoretical basis for the analysis of multiversion software subject to coincident errors,

    D. Eckhardt and L. Lee, “A theoretical basis for the analysis of multiversion software subject to coincident errors,”IEEE Transactions on Software Engineering, vol. SE-11, no. 12, pp. 1511–1517, 1985

    1985

  6. [6]

    Conceptual modeling of coincident failures in multiversion software,

    B. Littlewood and D. R. Miller, “Conceptual modeling of coincident failures in multiversion software,”IEEE Transactions on software engi- neering, vol. 15, no. 12, p. 1596, 1989

    1989

  7. [7]

    An empirical study of failure probabilities in multi-version software,

    J. C. Knight and N. G. Leveson, “An empirical study of failure probabilities in multi-version software,” inFault Tolerant Computing Symposium, vol. 16, 1986, pp. 165–170

    1986

  8. [8]

    Software diversity as a measure for reducing development risk,

    P. Popov, A. Povyakalo, V . Stankovic, and L. Strigini, “Software diversity as a measure for reducing development risk,” in2014 Tenth European Dependable Computing Conference. IEEE, 2014, pp. 106– 117

    2014

  9. [9]

    An experimental evaluation on reli- ability features of n-version programming,

    X. Cai, M. R. Lyu, and M. A. V ouk, “An experimental evaluation on reli- ability features of n-version programming,” in16th IEEE International Symposium on Software Reliability Engineering (ISSRE’05). IEEE, 2005, pp. 1–10

    2005

  10. [10]

    The effectiveness of software diversity in a large population of programs,

    M. J. Van der Meulen and M. A. Revilla, “The effectiveness of software diversity in a large population of programs,”IEEE Transactions on Software Engineering, vol. 34, no. 6, p. 753, 2008

    2008

  11. [11]

    Galapagos: Automated n-version programming with llms,

    J. Ron, D. Gaspar, J. Cabrera-Arteaga, B. Baudry, and M. Monper- rus, “Galapagos: Automated n-version programming with llms,”arXiv preprint arXiv:2408.09536, 2024

    arXiv 2024

  12. [12]

    How diversely can language models solve problems? exploring the algorithmic diversity of model-generated code,

    S. Lee, H. Chon, J. Jang, D. Lee, and H. Yu, “How diversely can language models solve problems? exploring the algorithmic diversity of model-generated code,”arXiv preprint arXiv:2503.00691, 2025

    Pith/arXiv arXiv 2025

  13. [13]

    Multi-programming language ensemble for code generation in large language model,

    T. Xue, X. Li, T. Azim, R. Smirnov, J. Yu, A. Sadrieh, and B. Pahlavan, “Multi-programming language ensemble for code generation in large language model,”arXiv preprint arXiv:2409.04114, 2024

    Pith/arXiv arXiv 2024

  14. [14]

    Evaluating the diversity and quality of llm generated content,

    A. Shypula, S. Li, B. Zhang, V . Padmakumar, K. Yin, and O. Bastani, “Evaluating the diversity and quality of llm generated content,”arXiv preprint arXiv:2504.12522, 2025

    arXiv 2025

  15. [15]

    Programming challenges: The pro- gramming contest training manual,

    S. S. Skiena and M. A. Revilla, “Programming challenges: The pro- gramming contest training manual,”Acm SIGACT News, vol. 34, no. 3, pp. 68–74, 2003

    2003

  16. [16]

    An experimental evaluation of the assumption of independence in multiversion programming,

    J. C. Knight and N. G. Leveson, “An experimental evaluation of the assumption of independence in multiversion programming,”IEEE Transactions on Software Engineering, vol. SE-12, no. 1, pp. 96–109, 1986

    1986

  17. [17]

    An experimental evaluation of software redundancy as a strategy for improving reliability,

    D. E. Eckhardt Jr, A. K. Caglayan, J. C. Knight, L. D. Lee, D. F. McAllister, M. A. V ouk, and J. P. Kelly, “An experimental evaluation of software redundancy as a strategy for improving reliability,” Tech. Rep., 1990

    1990

  18. [18]

    The effectiveness of choice of programming language as a diversity seeking decision,

    M. J. van der Meulen and M. Revilla, “The effectiveness of choice of programming language as a diversity seeking decision,” inEuropean Dependable Computing Conference. Springer, 2005, pp. 199–209

    2005

  19. [19]

    An empirical study of the effectiveness of

    P. Popov, V . Stankovic, and L. Strigini, “An empirical study of the effectiveness of” forcing” diversity based on a large population of diverse programs,” in2012 IEEE 23rd International Symposium on Software Reliability Engineering. IEEE, 2012, pp. 41–50

    2012

  20. [20]

    The impact of ai on developer productivity: Evidence from github copilot,

    S. Peng, E. Kalliamvakou, P. Cihon, and M. Demirer, “The impact of ai on developer productivity: Evidence from github copilot,”arXiv preprint arXiv:2302.06590, 2023

    Pith/arXiv arXiv 2023

  21. [21]

    Productivity assessment of neural code completion,

    A. Ziegler, E. Kalliamvakou, X. A. Li, A. Rice, D. Rifkin, S. Simister, G. Sittampalam, and E. Aftandilian, “Productivity assessment of neural code completion,” inProceedings of the 6th ACM SIGPLAN interna- tional symposium on machine programming, 2022, pp. 21–29

    2022

  22. [22]

    A survey on evaluating large language models in code generation tasks,

    L. Chen, Q. Guo, H. Jia, Z. Zeng, X. Wang, Y . Xu, J. Wu, Y . Wang, Q. Gao, J. Wanget al., “A survey on evaluating large language models in code generation tasks,”arXiv preprint arXiv:2408.16498, 2024

    Pith/arXiv arXiv 2024

  23. [23]

    Towards an understanding of large language models in software engineering tasks,

    Z. Zheng, K. Ning, Q. Zhong, J. Chen, W. Chen, L. Guo, W. Wang, and Y . Wang, “Towards an understanding of large language models in software engineering tasks,”Empirical Software Engineering, vol. 30, no. 2, p. 50, 2025

    2025

  24. [24]

    Evaluating large language models trained on code,

    M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. D. O. Pinto, J. Kaplan, H. Edwards, Y . Burda, N. Joseph, G. Brockmanet al., “Evaluating large language models trained on code,”arXiv preprint arXiv:2107.03374, 2021

    Pith/arXiv arXiv 2021

  25. [25]

    Program synthesis with large language models,

    J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Leet al., “Program synthesis with large language models,”arXiv preprint arXiv:2108.07732, 2021

    Pith/arXiv arXiv 2021

  26. [26]

    Measuring coding challenge competence with apps,

    D. Hendrycks, S. Basart, S. Kadavath, M. Mazeika, A. Arora, E. Guo, C. Burns, S. Puranik, H. He, D. Songet al., “Measuring coding challenge competence with apps,” inThirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021

    2021

  27. [27]

    xcodeeval: A large scale multilingual multitask benchmark for code understanding, generation, translation and retrieval,

    M. A. M. Khan, M. S. Bari, X. L. Do, W. Wang, M. R. Parvez, and S. Joty, “xcodeeval: A large scale multilingual multitask benchmark for code understanding, generation, translation and retrieval,”arXiv preprint arXiv:2303.03004, 2023

    Pith/arXiv arXiv 2023

  28. [28]

    Towards understanding the characteristics of code generation errors made by large language models,

    Z. Wang, Z. Zhou, D. Song, Y . Huang, S. Chen, L. Ma, and T. Zhang, “Towards understanding the characteristics of code generation errors made by large language models,” in2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE). IEEE, 2025, pp. 2587– 2599

    2025

  29. [29]

    Bugs in large language models generated code: An empirical study,

    F. Tambon, A. Moradi-Dakhel, A. Nikanjam, F. Khomh, M. C. Des- marais, and G. Antoniol, “Bugs in large language models generated code: An empirical study,”Empirical Software Engineering, vol. 30, no. 3, p. 65, 2025

    2025

  30. [30]

    No need to lift a finger anymore? assessing the quality of code generation by chatgpt,

    Z. Liu, Y . Tang, X. Luo, Y . Zhou, and L. F. Zhang, “No need to lift a finger anymore? assessing the quality of code generation by chatgpt,” IEEE Transactions on Software Engineering, vol. 50, no. 6, pp. 1548– 1584, 2024

    2024

  31. [31]

    Analyzing the dependability of large language models for code clone generation,

    A. Eagal, K. T. Stolee, and J.-P. Ore, “Analyzing the dependability of large language models for code clone generation,”Journal of Systems and Software, vol. 230, p. 112548, 2025. 12

    2025