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REVIEW 4 major objections 6 minor 108 references

Design-System-Aware Development with AI: Evaluating Productivity and Design Consistency

T0 review · 4 major / 6 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read A design-system-aware AI assistant cut front-end task completion time by 46.7% to 69.4% in an industrial experiment with 49 professional developers.

desk verdict Genuinely new three-condition industrial comparison, but the headline p-value is unverifiable as written — a reporting problem, not a fatal one. read the letter →

arxiv 2607.13156 v1 pith:QKOULMI7 submitted 2026-07-14 cs.SE

Luciane Silva , Thayssa Rocha , Nicole Davila , Gustavo Pinto This is my paper
classification cs.SE
keywords DesignSystemsAI-assisteddevelopmentfront-endproductivitytime-to-deliverytaskcompletenesscontrolledexperimentAngulariOS
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that AI tools which are explicitly aware of an enterprise design system substantially accelerate front-end development and improve design consistency, beyond what the design system alone achieves. It reports a controlled experiment at a large Brazilian company where developers implemented two high-fidelity screens across Angular, iOS, and Android, either manually, with a design system, or with a design-system-aware AI assistant. The central claim is that AI assistance reduced average time-to-delivery by roughly half to two-thirds, raised task completeness from 68% (manual) and 85% (design-system-only) to 96%, and shrank performance variability. A sympathetic reader should care because direct industrial evidence on AI plus design-system workflows has been scarce, and the results suggest concrete productivity and consistency gains for real-world front-end teams.

What carries the argument

The central mechanism is a controlled industrial experiment with an incremental-submission protocol: developers submit each implemented screen section as they finish, and specialists review in real time, recording exact completion times and rejecting non-conforming submissions until correct. The intervention is a design-system-aware AI assistant—an internal tool given the enterprise design system as context—contrasted against manual work and design-system-only work. This setup is what lets the authors attribute differences in time, completeness, and variability to the AI tool rather than to differences in task difficulty.

What would settle it

Re-run the experiment with random assignment to condition, equal group sizes in every cycle, and a pre-test of developer proficiency on a comparable task; if the time reduction disappears or loses statistical significance after controlling for proficiency, the central claim collapses.

Watch

Extended reading notes

Core claim

In a controlled between-subjects experiment conducted over two cycles with 49 professional developers, the use of a design-system-aware AI assistant significantly reduced completion time compared to both a manual baseline and design-system-only development (p<0.05). Average time-to-delivery fell from 536 to 164 minutes in Angular (69.4%), from 593 to 316 minutes in iOS (46.7%), and from 388 to 163 minutes in Android (57.9%); reductions against the design-system-only condition ranged from 15.5% to 24.4%. AI assistance also raised average task completeness to 96%, versus 85% with the design system alone and 68% manually, and reduced the standard deviation of completion times across all stacks.

Load-bearing premise

The load-bearing premise is that the three groups were equivalent in developer skill, motivation, and working conditions, because participants were balanced but not randomly assigned, and the manual baseline came from a smaller group that ran only in the first cycle.

Editorial extensions

If this is right

  • If the central claim holds, organizations with enterprise design systems can expect measurable time-to-delivery reductions of roughly 15–25% over design-system-only workflows, and 46–69% over fully manual workflows, when adopting a context-aware AI assistant.
  • Engineering leaders can make stack-specific adoption decisions: the evidence suggests Angular benefits most from complexity reduction, iOS from variability smoothing, and Android from incremental gains on an already efficient baseline.
  • Task completeness rising to 96% supports the idea that design-system-aware AI can improve visual fidelity and reduce rework caused by non-compliant implementations.
  • Lower standard deviations in completion time imply more predictable delivery schedules, which matters for planning in regulated or large-portfolio environments.
  • The break-pattern results, if reliable, imply reduced workflow friction and potentially lower cognitive load during design-to-code translation, which could improve developer experience and sustainability.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's results suggest that the key driver of the productivity gain is not generic AI code completion, but the embedding of organizational design knowledge into the tool's context; if true, similar gains might be replicable with any AI assistant that is given sufficient design-system context, not just the specific tool studied.
  • The measured time reductions include natural work pauses, so the reported 'time-to-delivery' may overstate pure coding speed gains; a more precise estimate of active development time could change the magnitude of the claimed effect.
  • The sharp reduction in break durations (from up to 195 minutes manually to at most 90 minutes with AI) is an indirect cognitive-load signal; directly measuring mental effort with physiological or self-report instruments would test the paper's implicit claim about reduced cognitive load.
  • The experiment used a single two-screen task, so the long-term effects on maintenance, onboarding, and design-governance compliance remain untested; a longitudinal study with varied task types would clarify whether the gains persist in production workflows.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper reports a controlled between-subjects experiment conducted at Zup Innovation (a large Brazilian enterprise) comparing three front-end development workflows: manual development, Design-System-only development, and DS-aware AI-assisted development, across Angular, iOS, and Android stacks. Based on two experimental cycles, the authors claim that AI assistance significantly reduced time-to-delivery by 46.7%–69.4% relative to manual development, increased task completeness from 68% (manual) to 85% (DS-only) to 96% (AI-assisted), and reduced performance variability. The paper also reports qualitative break-pattern analysis as evidence of reduced workflow friction and cognitive load. The central statistical claim (Section 3.1) is that AI-assisted development achieved p<0.05 compared with both manual and DS-only conditions, but the manuscript provides no test statistics, confidence intervals, effect sizes, or per-stack sample sizes.

Significance. If the findings withstand scrutiny, this would be a valuable industrial empirical contribution: it directly measures a DS-aware AI assistant in a real enterprise setting across multiple technology stacks, uses a controlled task with incremental submission checkpoints, and provides stack-specific lessons (Angular benefits from complexity reduction, iOS from variability smoothing, Android from incremental gains). The study has low circularity risk because the outcome is a direct measurement with no fitted parameters or outcome-defining equations. However, the central claim is currently not checkable from the reported statistics, and the group-equivalence assumption is insufficiently supported. The paper would be much stronger if it reported full inferential details, per-cell sample sizes, a Cycle-1-only analysis, and explicit discussion of the non-random assignment threat.

major comments (4)
  1. [Section 3.1 and Table 3] The central claim that AI assistance 'significantly reduced completion time' (p<0.05) cannot be independently verified because no test name, test statistic, degrees of freedom, exact p-value, confidence interval, effect size, or per-cell sample size is reported. The only p-value appears in prose. Please report the exact test used (e.g., Mann-Whitney U, Welch t), its statistic, exact p-value, effect size (e.g., Cliff's delta or Hedges' g), and per-stack per-group n. In addition, because the Manual group (n=7) ran only in Cycle 1 while DS and AI groups pool both cycles (Section 2.1), any pooled comparison confounds group with cycle. Provide a Cycle-1-only analysis and a cycle-equivalence check (e.g., DS/AI means by cycle).
  2. [Section 2.1] Group equivalence is load-bearing for the causal interpretation. The paper states participants were 'balanced by their technical stack and seniority level' but reports no random assignment procedure, no per-stack cell sizes, and no baseline measures (e.g., years of experience, prior familiarity with StackSpot AI or the DS). If assignment was not random, the observed 46.7%–69.4% reductions could reflect participant composition rather than the tool. Provide a demographic/experience table, the assignment mechanism, and equivalence tests between groups. This threat is not acknowledged in Section 4.3, which is a notable omission.
  3. [Sections 2.3 and 3.1 (Table 2)] Task completeness was assessed by specialists, but the manuscript gives no scoring rubric, number of reviewers, blinding procedure, or inter-rater reliability. Table 2 reports only group means (68%, 85%, 96%) with no variance, confidence interval, or test statistic. The claim of 'higher average completeness' is therefore unsupported as presented. Please specify how completeness was operationalized, whether multiple specialists independently rated the deliverables, and provide a statistical comparison (including per-stack and per-cycle breakdowns).
  4. [Section 3.3] Break patterns are used to infer reduced workflow friction and cognitive load, but no quantitative analysis is reported. The cited ranges overlap (AI 0–90 minutes vs. DS 60–110 minutes), and the claim of 'shorter and less frequent breaks' is not accompanied by any table, distribution, or test. If this secondary evidence is retained, report break frequency and duration summaries by group and stack, and clearly label this component as exploratory rather than confirmatory.
minor comments (6)
  1. [Tables 1 and 3] Tables 1 and 3 present the same productivity means and SDs. Consolidate them or cross-reference to avoid redundancy.
  2. [Section 3.3] 'R3 dataset' is mentioned without definition. Clarify whether R3 refers to a specific cycle, task section, or subgroup.
  3. [Section 1] The text contains a formatting artifact: 'followingRe' in 'investigates the followingRe' should read 'the following research question'.
  4. [Section 2.1] The experiment is described as 'controlled' but no random assignment is stated. Use 'quasi-experiment' or explicitly describe the assignment mechanism.
  5. [Abstract/Introduction] The abstract reports percentages without indicating they are relative reductions. Clarify that 46.7%, 57.9%, and 69.4% are reductions relative to manual time-to-delivery.
  6. [General] Exact p-values should replace the single 'p<0.05' statement, and confidence intervals for the mean reductions would materially improve interpretability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central result is a direct measured contrast, not derived from fitted inputs or self-cited theory.

full rationale

The paper makes no formal derivation and contains no equations that could reduce a prediction to an input. The reported productivity gains are direct observations of completion time and task completeness in three experimental arms (Manual, Design System, AI-Assisted). No parameter is fitted to the outcome and then renamed as a prediction; the AI-assisted condition is defined independently of the measured times. The only self-citations ([3] Davila et al., [6] Pinto et al.) appear in Related Work as contextual motivation and are not used to establish the experimental result or to justify any uniqueness/choice. There is therefore no self-citation chain that forces the conclusion. The main threats—small first-cycle-only manual group, no random assignment or group-equivalence data, unreported test statistics—are validity and reporting weaknesses, not circularity: they concern whether the comparison is unbiased, not whether the result is equivalent to its inputs by construction. The Limitations section does mention several threats to validity but omits the group-equivalence threat; this is a completeness gap, not a circular step. Under the stated rules, the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The central claim is empirical; what it rests on are the design and measurement assumptions above rather than mathematical axioms or fitted constants. No free parameters or invented entities appear.

assumptions (5)
  • domain assumption Balancing participants by stack and seniority adequately controls between-group differences in skill.
    Section 2.1 states participants were balanced but does not report random assignment or verification of group equivalence; if this fails, time differences may be due to ability rather than tool.
  • domain assumption Time-to-delivery inclusive of pauses is a valid proxy for productivity.
    Section 2.3 defines the metric as total time including natural work pauses; pauses may reflect unrelated factors.
  • domain assumption Specialist real-time review provides reliable completeness and completion-time measurements.
    Section 2.2 describes specialists reviewing incrementally; the paper acknowledges subjectivity in Section 4.3, yet relies on it for the dependent measure.
  • domain assumption Pooling data across both experimental cycles is valid for comparisons involving the manual baseline.
    Section 3.1 says analyses consolidate both rounds, but the manual group was only in the first cycle, creating a possible cycle confound.
  • domain assumption StackSpot AI was genuinely aware of the enterprise design system.
    Section 2.1 asserts the internal tool was contextualized with the DS; no technical verification or system description is given.

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Cite this review

Pith. "Pith review of Design-System-Aware Development with AI: Evaluating Productivity and Design Consistency." pith.science (2026). https://pith.science/paper/QKOULMI7

@misc{pith2026260713156,
  author       = {Pith},
  title        = {Pith review of: Design-System-Aware Development with AI: Evaluating Productivity and Design Consistency},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QKOULMI7}},
  note         = {Machine review of arXiv:2607.13156}
}
read the original abstract

Design Systems (DS) help standardize front-end development, yet developers still face challenges when translating high-fidelity mockups into consistent, production-ready interfaces. Although AI-assisted tools have emerged as a potential solution, empirical evidence on their effectiveness within DS-centered workflows remains limited. This paper reports a controlled experiment conducted at a large Brazilian enterprise that compares manual development, DS-only development, and DS-aware AI-assisted development across Angular, iOS, and Android stacks. Results from two experimental cycles show that AI assistance significantly reduced time-to-delivery (by 46.7% to 69.4%), increased task completeness, and decreased performance variability. Analysis of break patterns further suggests reduced workflow friction and smoother task execution. These findings provide empirical evidence that DS-aware AI tools can significantly accelerate development, improve design fidelity, and yield practical benefits for industrial front-end workflows.

Figures

Figures reproduced from arXiv: 2607.13156 by the authors.

Figure 1
Figure 1. Example of a high-fidelity mockup used in the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

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Works this paper leans on

108 extracted references · 2 linked inside Pith

  1. [1]

    Abril and Robert Plant

    Patricia S. Abril and Robert Plant. The patent holder's dilemma: Buy, sell, or troll?. Communications of the ACM. doi:10.1145/1188913.1188915

    arXiv

  2. [2]

    Deciding equivalances among conjunctive aggregate queries

    Sarah Cohen and Werner Nutt and Yehoshua Sagic. Deciding equivalances among conjunctive aggregate queries. doi:10.1145/1219092.1219093

    arXiv

  3. [3]

    Special issue: Digital Libraries. 1996

    1996

  4. [4]

    Understanding Policy-Based Networking

    David Kosiur. Understanding Policy-Based Networking

  5. [7]

    doi:10.1007/3-540-09237-4

    The title of book two. doi:10.1007/3-540-09237-4

    doi:10.1007/3-540-09237-4

  6. [8]

    Asad Z. Spector. Achieving application requirements. Distributed Systems. doi:10.1145/90417.90738

    arXiv

  7. [9]

    Douglass and David Harel and Mark B

    Bruce P. Douglass and David Harel and Mark B. Trakhtenbrot. Statecarts in use: structured analysis and object-orientation. Lectures on Embedded Systems. doi:10.1007/3-540-65193-4_29

    doi:10.1007/3-540-65193-4_29

  8. [10]

    Donald E. Knuth. The Art of Computer Programming, Vol. 1: Fundamental Algorithms (3rd. ed.)

Show all 108 references
  1. [11]

    Donald E. Knuth. The Art of Computer Programming

  2. [12]

    Structured Variational Inference Procedures and their Realizations (as incol)

    Dan Geiger and Christopher Meek. Structured Variational Inference Procedures and their Realizations (as incol). Proceedings of Tenth International Workshop on Artificial Intelligence and Statistics, The Barbados

  3. [13]

    Stan W. Smith. An experiment in bibliographic mark-up: Parsing metadata for XML export. Proceedings of the 3rd. annual workshop on Librarians and Computers

  4. [14]

    Catch me, if you can: Evading network signatures with web-based polymorphic worms

    Matthew Van Gundy and Davide Balzarotti and Giovanni Vigna. Catch me, if you can: Evading network signatures with web-based polymorphic worms. Proceedings of the first USENIX workshop on Offensive Technologies

  5. [15]

    Predicate Path expressions

    Sten Andler. Predicate Path expressions. Proceedings of the 6th. ACM SIGACT-SIGPLAN symposium on Principles of Programming Languages. doi:10.1145/567752.567774

  6. [16]

    LOGICS of Programs: AXIOMATICS and DESCRIPTIVE POWER

    David Harel. LOGICS of Programs: AXIOMATICS and DESCRIPTIVE POWER

  7. [17]

    Anisi , title =

    David A. Anisi , title =

  8. [18]

    Clarkson

    Kenneth L. Clarkson. Algorithms for Closest-Point Problems (Computational Geometry)

  9. [19]

    Introduction to Bayesian Statistics

    Harry Thornburg. Introduction to Bayesian Statistics. 2001

    2001

  10. [20]

    CLIFFORD: a Maple 11 Package for Clifford Algebra Computations, version 11

    Rafal Ablamowicz and Bertfried Fauser. CLIFFORD: a Maple 11 Package for Clifford Algebra Computations, version 11. 2007

    2007

  11. [21]

    Stats and Analysis

    Poker-Edge.Com. Stats and Analysis. 2006

    2006

  12. [22]

    A more perfect union

    Barack Obama. A more perfect union

  13. [23]

    The fountain of youth

    Joseph Scientist. The fountain of youth

  14. [24]

    Solder man

    Dave Novak. Solder man. ACM SIGGRAPH 2003 Video Review on Animation theater Program: Part I - Vol. 145 (July 27--27, 2003). doi:10.945/woot07-S422

    2003

  15. [25]

    Interview with Bill Kinder: January 13, 2005

    Newton Lee. Interview with Bill Kinder: January 13, 2005. Comput. Entertain. doi:10.1145/1057270.1057278

    2005

  16. [26]

    The Enabling of Digital Libraries

    Bernard Rous. The Enabling of Digital Libraries. Digital Libraries

  17. [28]

    (new) Finding minimum congestion spanning trees , journal =

    Werneck, Renato and Setubal, Jo\. (new) Finding minimum congestion spanning trees , journal =. doi:10.1145/351827.384253 , acmid = 384253, publisher =

  18. [30]

    and Mei, Alessandro , title =

    Conti, Mauro and Di Pietro, Roberto and Mancini, Luigi V. and Mei, Alessandro , title =. Inf. Fusion , volume =. 2009 , issn =. doi:10.1016/j.inffus.2009.01.002 , acmid =

    2009 doi

  19. [31]

    and Hutchful, David K

    Li, Cheng-Lun and Buyuktur, Ayse G. and Hutchful, David K. and Sant, Natasha B. and Nainwal, Satyendra K. , title =. CHI '08 extended abstracts on Human factors in computing systems , year =. doi:10.1145/1358628.1358946 , acmid =

  20. [32]

    , title =

    Hollis, Billy S. , title =. 1999 , isbn =

    1999

  21. [33]

    Goossens, Michel and Rahtz, S. P. and Moore, Ross and Sutor, Robert S. , title =. 1999 , isbn =

    1999

  22. [34]

    and Rosenberg, Arnold L

    Buss, Jonathan F. and Rosenberg, Arnold L. and Knott, Judson D. , title =. 1987 , source =

    1987

  23. [35]

    CHI '08: CHI '08 extended abstracts on Human factors in computing systems , year =

    , note =. CHI '08: CHI '08 extended abstracts on Human factors in computing systems , year =

  24. [36]

    Algorithms for Closest-Point Problems (Computational Geometry) , year =

    Clarkson, Kenneth Lee , advisor =. Algorithms for Closest-Point Problems (Computational Geometry) , year =

  25. [37]

    SIGCOMM Comput. Commun. Rev. , year =

  26. [38]

    2004 , isbn =

    IEEE TCSC Executive Committee , booktitle =. 2004 , isbn =. doi:http://dx.doi.org/10.1109/ICWS.2004.64 , acmid =

    2004 doi

  27. [39]

    Distributed systems (2nd Ed.) , year =

  28. [40]

    , title =

    Petrie, Charles J. , title =. 1986 , source =

    1986

  29. [41]

    Donald E. Knuth. Seminumerical Algorithms. 1981

    1981

  30. [42]

    E-commerce and cultural values , year =

    Kong, Wei-Chang , Title =. E-commerce and cultural values , year =

  31. [43]

    E-commerce and cultural values , year =

    Kong, Wei-Chang , type =. E-commerce and cultural values , year =

  32. [44]

    Chapter 9 , booktitle =

    Kong, Wei-Chang , editor =. Chapter 9 , booktitle =

  33. [45]

    E-commerce and cultural values , editor =

    Kong, Wei-Chang , title =. E-commerce and cultural values , editor =. 2003 , isbn =

    2003

  34. [46]

    E-commerce and cultural values - (InBook-num-in-chap) , chapter =

    Kong, Wei-Chang , editor =. E-commerce and cultural values - (InBook-num-in-chap) , chapter =. 2004 , address =

    2004

  35. [47]

    E-commerce and cultural values (Inbook-text-in-chap) , chapter =

    Kong, Wei-Chang , editor =. E-commerce and cultural values (Inbook-text-in-chap) , chapter =. 2005 , address =

    2005

  36. [48]

    E-commerce and cultural values (Inbook-num chap) , chapter =

    Kong, Wei-Chang , editor =. E-commerce and cultural values (Inbook-num chap) , chapter =. 2006 , address =

    2006

  37. [49]

    Microelectron

    Mehdi Saeedi and Morteza Saheb Zamani and Mehdi Sedighi , title =. Microelectron. J. , volume =. 2010 , pages =

    2010

  38. [50]

    Mehdi Saeedi and Morteza Saheb Zamani and Mehdi Sedighi and Zahra Sasanian , title =. J. Emerg. Technol. Comput. Syst. , volume =

  39. [51]

    Kirschmer, Markus and Voight, John , title =. SIAM J. Comput. , issue_date =. 2010 , issn =. doi:https://doi.org/10.1137/080734467 , acmid =

    2010 doi

  40. [52]

    Hoare, C. A. R. , title =. Structured programming (incoll) , editor =. 1972 , isbn =

    1972

  41. [53]

    History of programming languages I (incoll) , editor =

    Lee, Jan , title =. History of programming languages I (incoll) , editor =. 1981 , isbn =. doi:http://doi.acm.org/10.1145/800025.1198348 , acmid =

    1981

  42. [54]

    , title =

    Dijkstra, E. , title =. Classics in software engineering (incoll) , year =

  43. [55]

    , title =

    Wenzel, Elizabeth M. , title =. Multimedia interface design (incoll) , year =. doi:10.1145/146022.146089 , acmid =

  44. [56]

    , title =

    Mumford, E. , title =. Critical issues in information systems research (incoll) , year =

  45. [57]

    and Golden, Donald G

    McCracken, Daniel D. and Golden, Donald G. , title =. 1990 , isbn =

    1990

  46. [58]

    The analysis of linear partial differential operators

    H. The analysis of linear partial differential operators. 1985 , PAGES =

    1985

  47. [59]

    IEEE", address =

    A. Adya and P. Bahl and J. Padhye and A.Wolman and L. Zhou , title =. Proceedings of the IEEE 1st International Conference on Broadnets Networks (BroadNets'04) , publisher = "IEEE", address = "Los Alamitos, CA", year =

  48. [60]

    I. F. Akyildiz and W. Su and Y. Sankarasubramaniam and E. Cayirci , title =. Comm. ACM , volume = 38, number = "4", year =

  49. [61]

    I. F. Akyildiz and T. Melodia and K. R. Chowdhury , title =. Computer Netw. , volume = 51, number = "4", year =

  50. [62]

    ACM", address =

    P. Bahl and R. Chancre and J. Dungeon , title =. Proceeding of the 10th International Conference on Mobile Computing and Networking (MobiCom'04) , publisher = "ACM", address = "New York, NY", year =

  51. [63]

    8 (Special Issue on Sensor Networks)

    D. Culler and D. Estrin and M. Srivastava , title =. IEEE Comput. , volume = 37, number = "8 (Special Issue on Sensor Networks)", publisher = "IEEE", address = "Los Alamitos, CA", year =

  52. [64]

    Natarajan and M

    A. Natarajan and M. Motani and B. de Silva and K. Yap and K. C. Chua , title =. Network Architectures , editor =. 960935712

  53. [65]

    Tzamaloukas and J

    A. Tzamaloukas and J. J. Garcia-Luna-Aceves , title =

  54. [66]

    Zhou and J

    G. Zhou and J. Lu and C.-Y. Wan and M. D. Yarvis and J. A. Stankovic , title =

  55. [67]

    Mapping Powerlists onto Hypercubes

    Jacob Kornerup. Mapping Powerlists onto Hypercubes. 1994

    1994

  56. [68]

    Automatic Parallelization for Distributed-Memory Multiprocessing Systems

    Michael Gerndt. Automatic Parallelization for Distributed-Memory Multiprocessing Systems

  57. [69]

    J. E. Archer, Jr. and R. Conway and F. B. Schneider. User recovery and reversal in interactive systems. ACM Trans. Program. Lang. Syst

  58. [70]

    D. D. Dunlop and V. R. Basili. Generalizing specifications for uniformly implemented loops. ACM Trans. Program. Lang. Syst

  59. [71]

    Heering and P

    J. Heering and P. Klint. Towards monolingual programming environments. ACM Trans. Program. Lang. Syst

  60. [72]

    Donald E. Knuth. The book

  61. [73]

    Korach and D

    E. Korach and D. Rotem and N. Santoro. Distributed algorithms for finding centers and medians in networks. ACM Trans. Program. Lang. Syst

  62. [74]

    : A Document Preparation System

    Leslie Lamport. : A Document Preparation System

  63. [75]

    F. Nielson. Program transformations in a denotational setting. ACM Trans. Program. Lang. Syst

  64. [76]

    Brian K. Reid. A high-level approach to computer document formatting. Proceedings of the 7th Annual Symposium on Principles of Programming Languages

  65. [77]

    and Abdelzaher, Tarek F

    Zhou, Gang and Wu, Yafeng and Yan, Ting and He, Tian and Huang, Chengdu and Stankovic, John A. and Abdelzaher, Tarek F. , title =. ACM Trans. Embed. Comput. Syst. , issue_date =. doi:10.1145/1721695.1721705 , acmid = 1721705, publisher =

  66. [78]

    Institutional members of the Users Group

  67. [79]

    Boris Veytsman , title =

  68. [80]

    Robin Schneider , title =

  69. [81]

    and Peterson, Larry L

    Bowman, Mic and Debray, Saumya K. and Peterson, Larry L. , title =. ACM Trans. Program. Lang. Syst. , volume =. 1993 , doi =

    1993

  70. [82]

    TUGboat , volume =

    Braams, Johannes , title =. TUGboat , volume =

  71. [83]

    Post Congress Tristesse

    Malcolm Clark. Post Congress Tristesse. TeX90 Conference Proceedings

  72. [84]

    ACM Trans

    Herlihy, Maurice , title =. ACM Trans. Program. Lang. Syst. , volume =. 1993 , doi =

    1993

  73. [85]

    Salas and Einar Hille

    S.L. Salas and Einar Hille. Calculus: One and Several Variable. 1978

    1978

  74. [86]

    Publication quality tables in

    Simon Fear , month =. Publication quality tables in

  75. [87]

    Using the amsthm Package , organization =

  76. [88]

    2019 , url =

    R: A Language and Environment for Statistical Computing , author =. 2019 , url =

    2019

  77. [89]

    Sam Anzaroot and Andrew McCallum , title =

  78. [90]

    Brad and Haunschild, Robin , title =

    Bornmann, Lutz and Wray, K. Brad and Haunschild, Robin , title =

  79. [91]

    2014 , archivePrefix =

    Sam Anzaroot and Alexandre Passos and David Belanger and Andrew McCallum , title =. 2014 , archivePrefix =. 1403.1349 , doi =

    2014 arXiv

  80. [92]

    Proceedings of the 20th International Colloquium on Automata, Languages and Programming , series =

    Maintaining Discrete Probability Distributions Optimally , author =. Proceedings of the 20th International Colloquium on Automata, Languages and Programming , series =

  81. [93]

    Reiser , year = 2014, title =

    Brian J. Reiser , year = 2014, title =

    2014

  82. [94]

    Baggett and M

    R. Baggett and M. Simecek and C. Chambellan and K. Tsui and M. Fraune , year = 2025, title =

    2025

  83. [95]

    The Computational Geometry Algorithms Library , author =

  84. [96]

    Menelaos Karavelas , subtitle =

  85. [97]

    The Computational Geometry Algorithms Library , subtitle =

    Menelaos Karavelas , editor =. The Computational Geometry Algorithms Library , subtitle =

  86. [98]

    The Parmap library , author =

  87. [99]

    Christopher Anderson and Sophia Drossopoulou , title =

  88. [100]

    2023 , journal =

    Ebert, Christof and Louridas, Panos , title =. 2023 , journal =

    2023

  89. [101]

    The state of AI in 2025: Agents, innovation, and transformation , author =

    2025

  90. [102]

    arXiv preprint arXiv:2503.22625 , year=

    Challenges and paths towards ai for software engineering , author=. arXiv preprint arXiv:2503.22625 , year=

    arXiv

  91. [103]

    2024 , isbn =

    Pinto, Gustavo and De Souza, Cleidson and Rocha, Thayssa and Steinmacher, Igor and Souza, Alberto and Monteiro, Edward , title =. 2024 , isbn =. doi:10.1145/3644815.3644949 , booktitle =

    2024

  92. [104]

    2024 , isbn =

    Pinto, Gustavo and De Souza, Cleidson and Neto, Joao Batista and Souza, Alberto and Gotto, Tarcisio and Monteiro, Edward , title =. 2024 , isbn =. doi:10.1145/3639477.3639751 , booktitle =

    2024

  93. [105]

    2025 IEEE/ACM 18th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE) , pages=

    Human and Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peers , author=. 2025 IEEE/ACM 18th International Conference on Cooperative and Human Aspects of Software Engineering (CHASE) , pages=. 2025 , organization=

    2025

  94. [106]

    2019 , edition =

    John Sweller and Paul Ayres and Slava Kalyuga , title =. 2019 , edition =

    2019

  95. [107]

    Glassman , title =

    Priyanka Vaithilingam and Tianyi Zhang and Elena L. Glassman , title =. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems , year =

    2022

  96. [108]

    Empirical Software Engineering , volume =

    Lamine, Yassine and Cheng, Jinghui , title =. Empirical Software Engineering , volume =. 2022 , publisher =

    2022

  97. [109]

    Queue , volume =

    Christian Bird and Sven Amann and Thomas Dullien and Sebastian Proksch , title =. Queue , volume =. 2023 , publisher =

    2023

  98. [110]

    IEEE Software , volume =

    Michael Fanguy and Jason Cranford and Sarah Fathallah , title =. IEEE Software , volume =. 2021 , doi =

    2021

  99. [111]

    2024 , booktitle =

    An Industry Case Study on Adoption of AI-based Programming , author =. 2024 , booktitle =

    2024

  100. [112]

    2024 , issue_date =

    Hou, Xinyi and Zhao, Yanjie and Liu, Yue and Yang, Zhou and Wang, Kailong and Li, Li and Luo, Xiapu and Lo, David and Grundy, John and Wang, Haoyu , title =. 2024 , issue_date =. doi:10.1145/3695988 , journal =

    2024 doi

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.