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REVIEW 4 major objections 6 minor 60 references

Citations in Software Engineering -- Paper-related, Journal-related, and Author-related Factors

T0 review · 4 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read The measurable predictors of a software engineering paper's citation count are venue, the team's past citations, length, reference count, and reference recency.

desk verdict First multivariate citation-factor study for SE with a real counterintuitive finding, but the advice section outruns the observational design. read the letter →

arxiv 1908.04122 v2 pith:NNH3ARSF submitted 2019-08-12 cs.DL cs.SE

Mika Mäntylä , Vahid Garousi This is my paper · ORCID
classification cs.DLcs.SE
keywords citationssoftwareengineeringbibliometricsnegativebinomialregressionquantilePriceindexpublicationvenuecitationfactors
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 aims to establish which measurable features of a software engineering journal paper predict how many citations it will receive, using all 25,113 papers published in 16 major SE journals between 1970 and 2018. It claims to be the first SE study to use multivariate regression rather than isolated pairwise comparisons, fitting both negative binomial regression for mean citations and quantile regression for median citations. The authors report that publication venue, the author team's past citations, paper length, the number of references, and the recency of references are the most influential factors, with paper age controlled throughout. They also report a counterintuitive result: once past citations are accounted for, an author team's total number of past papers is negatively associated with citations, meaning a short but highly cited track record predicts higher citations than a long publication list. A sympathetic reader would care because the findings turn the vague sense that extrinsic factors matter into a ranked, quantified list that could inform how research evaluations weight citations.

What carries the argument

The analytical engine is a pair of count-data regressions fitted to the full corpus with a common age control. Negative binomial regression models the expected mean citation count, and quantile regression at the median models the expected median, which matters because the distribution is skewed (mean 22.5 citations, median 6). Predictors are added stepwise and retained only when the Akaike Information Criterion improves, giving a principled ranking of factors. The Price index, defined as the share of a paper's references published in the five years before the paper, operationalizes reference recency and emerges as one of the strongest paper-level predictors.

What would settle it

Re-run the complete regression on a subset of software engineering papers independently rated for research rigor using a published rubric; if adding that rigor score drives the coefficients for venue, log page count, reference count, and Price index to near zero, then the paper's reported associations are confounded by unmeasured quality.

Watch

Extended reading notes

Core claim

The central claim is that citation counts of software engineering journal papers can be modeled jointly by paper age, venue, author-team history, and paper characteristics, and that the dominant predictors are venue, author team's past citations, log-transformed page count, number of references, and reference recency as measured by the Price index. Across the individual and complete models, for both mean and median citations, these factors are statistically significant at p < 0.0001, while author-team size loses significance in the complete mean model once venue and paper factors are added. A distinctive finding is the sign reversal of author-team productivity: with past citations in the model, more past papers is associated with fewer expected citations. The paper frames its contribution as the first robust multivariate analysis of citation factors in software engineering, extending earlier SE work that had treated factors in isolation.

Load-bearing premise

The load-bearing premise is that no unmeasured property like genuine technical quality is driving both the visible predictors (venue, length, references) and the citation counts, since the dataset contains no direct measure of paper quality and the authors explicitly exclude quality and novelty from the factors considered.

Editorial extensions

If this is right

  • Authors publishing in lower-impact venues face an uphill battle: venue alone shifts expected citations substantially even after controlling for age and other factors.
  • A short but highly cited publication track record predicts higher future citations than a long list of past papers with the same citation count.
  • Comprehensiveness pays: longer papers with more references and fresher references receive more citations, consistent with reciprocity and thorough literature coverage.
  • The standard two-year impact-factor window is ill-suited to software engineering, since citation accumulation continues for roughly 15 years for highly cited papers.
  • Mean and median citation models largely agree on the major factors, so the conclusions are not an artifact of the highly skewed citation distribution.

Reading between the lines

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

  • Because the dataset contains no direct measure of paper quality, the advice to write longer papers or aim for high-profile venues likely overstates causal control; a natural experiment such as a submission lottery or a page-limit discontinuity would be needed to separate author and venue effects from content quality.
  • The negative coefficient on past papers suggests a selective 'best work' signaling effect rather than a pure productivity effect; a testable extension would compare first-authored versus team-authored papers to see whether the association is an aggregation artifact.
  • The venue results imply that evaluators comparing SE papers across venues should normalize or weight citation counts by venue, an idea the authors mention but do not develop.
  • The Price index result may be partly circular: papers with recency-heavy reference lists tend to sit in fast-moving subfields, and controlling for publication age may not fully separate citing recent work from being in a fast-moving field.
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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 studies factors associated with citation counts of software engineering (SE) journal papers. Using a Scopus dataset of 25,113 papers from 16 SE journals published between 1970 and 2018, the authors fit negative binomial regression models (for the mean) and quantile regression models (for the median) to examine paper age, publication venue, journal impact-factor metrics, author-team characteristics, and paper properties such as length, reference count, reference recency, paper type, and title length. The headline findings are that venue, author team's past citations, paper length, number of references, and recency of references are the most influential factors, and the paper closes with prescriptive advice for researchers. The results are compared with two prior reviews of citation factors in other fields.

Significance. If the results hold, this would be a useful contribution as the first multivariate study of citation determinants specifically for SE journals, with a large corpus and two complementary regression approaches. The paper also provides a systematic comparison with earlier reviews in Table 23, and the authors are candid about several limitations, including missing TOSEM page data and the difficulty of disambiguating author profiles. The public availability of the analysis scripts is a further strength. However, the observational design and the construction of some predictors substantially limit the strength of the causal and prescriptive claims, and several technical issues in the impact-factor analysis and model comparison need to be resolved before the headline conclusions can be accepted at face value.

major comments (4)
  1. [Section 4.2, Tables 9, 10, 17, 19] The impact-factor models are circular in an important sense. Each journal's CiteScore, SNIP, and SJR values are computed from citations to papers in that journal, including the very papers in the dataset, and the authors assign the same average score to all papers of a journal for all years (Section 4.2 and Table 8). Regressing individual citation counts on these journal-level averages therefore introduces a mechanical correlation: the outcome of a paper contributes to the predictor assigned to that same paper. This affects the 'impact factor is a strong predictor' conclusion and the complete models in Tables 17 and 19. The dummy-variable venue models (Tables 16 and 18) are not subject to this problem and give broadly similar conclusions, which mitigates the concern, but the impact-factor results should either be re-derived with lagged or leave-one-out impact factors, or explicitly presented as descriptive only, with the circularity acknowledged in the interpretation.
  2. [Section 3.1, Table 1, Section 7] The complete regression models in Tables 16 and 18 control only for objective proxies and explicitly exclude 'subjective' factors such as paper quality, novelty, and study design for lack of data. The paper's headline claim that venue, author team's past citations, paper length, and reference characteristics are 'most influential' and the Section 7 advice ('aim for high-profile venues', 'write longer papers', 'build a high-quality author team') attribute causal or quasi-causal influence to variables that may merely track unmeasured paper quality. The authors do note in Section 7 that they cannot distinguish quality from halo effects, but the abstract and conclusions nevertheless present these as actionable recommendations. The manuscript should reframe the conclusions as associations, add a sensitivity discussion of likely omitted-variable directions, or both.
  3. [Section 3.4 and Tables 4-19] AIC is used throughout to compare quantile regression models, including the RQ1 and RQ5 model-selection decisions (e.g., AIC values reported in Sections 4.1 and 4.2). However, quantile regression is estimated by minimizing a sum of absolute deviations and does not have a likelihood function in the usual sense, so the stated formula AIC = 2k - 2ln(L) is not directly applicable. The authors should justify the use of AIC for quantile regression models, provide the exact quantity they computed, or replace it with a comparison criterion that is valid for quantile regression, such as cross-validated prediction error for the median.
  4. [Section 4.5, Table 18] In the complete quantile regression model, TOSEM has a coefficient of 33.93 with a standard error of 8.08, based on only 199 papers after excluding papers with missing page counts; the authors themselves note that this subset is biased toward older papers and makes the coefficient volatile. This large and unstable coefficient is nonetheless used in Section 5.2 and Table 23 as evidence about venue rankings. Given the acknowledged data problem, the TOSEM-specific numerical results in the complete quantile model should not be interpreted as a reliable venue effect, and the venue-ranking discussion should be correspondingly qualified.
minor comments (6)
  1. [Section 3.3, paragraph on negative binomial regression] The sentence 'negative binomial regression is better as it does not require transforming the independent variables to meet the requirement of the normal distribution that is precursory of independent variables in linear multiple regression' is unclear and should be rewritten; count regression does not require normality of the outcome or predictors.
  2. [Table 6 caption] The caption reads 'Coefficient of binomial negative regression'; this should be 'negative binomial regression'.
  3. [Section 5.2, first paragraph] The text says 'we compared tree metrics from Scopus'; this should be 'three metrics'.
  4. [Section 4.4] The subsection heading 'Paper (venue) Type' is confusing; the variable is the Scopus publication type, not a venue type, and the heading should be changed accordingly.
  5. [Section 4.3 and 4.4] The paper removes different numbers of papers for missing author, affiliation, country, and page-count data (e.g., 975, 1,282, 910), and AIC values are compared across models with different sample sizes at some points, but this is not consistently flagged. The authors should state the effective sample size for every model whose AIC is compared.
  6. [Table 23] The 'TP' column reports the number of models offering support, but the counting rule is not fully transparent; for example, author count is significant in some but not all models and the table entry '3 / 1' should be explained in the notes with reference to the exact models.

Circularity Check

1 steps flagged · score 4.0 of 10

Impact-factor venue models regress citations on a journal-level mean of citations, but the dummy-variable venue models independently support the main conclusion.

  1. self definitional [Section 4.2, 'Impact Factors of Venues' (Tables 9-10); also Section 3.1]
    "After all, the journal impact-factors are effectively measuring the mean number of citations of the articles, published in a particular journal, receive in the course of two years after they have been published. ... As the metrics were not available for all years, we first computed the mean scores of all metrics and assigned them to each journal for all years as in Table 8. For example, all TSE papers received the same CiteScore."

    The predictor is constructed from the response: CiteScore/SNIP/SJR are journal-level averages (or normalized measures) of the very citation counts being regressed. Each journal's average score is assigned to every paper in that journal, so a paper's own citations contribute to the journal average used to predict that paper's citations. This makes the positive impact-factor coefficients partly mechanical—the model regresses the outcome on an aggregate of the outcome. The paper even states that impact factors 'are effectively measuring the mean number of citations' of a journal's articles, so the 'impact factor predicts citations' result is partly true by definition rather than from independent information.

full rationale

The central derivation—negative binomial and quantile regressions of citation counts on age, venue dummies, author-team variables, and paper variables—is self-contained: none of these predictors is constructed from the response variable, and the paper compares models by AIC on the same observed data. The one genuinely circular element is the impact-factor variant of the venue model: CiteScore/SNIP/SJR are journal-level means (or normalized means) of citation counts, and the paper assigns each journal's average score to every paper in that journal, so a paper's own citations contribute to its own predictor. This makes the positive impact-factor coefficients in Tables 9-10 and 17/19 partially mechanical. However, the dummy-variable venue models (Tables 6-7 and the complete models in Tables 16 and 18) avoid outcome-derived predictors and yield the same broad conclusion that venue matters, so the paper's headline claim is not forced by the circular step. Omitted-variable concerns about unmeasured paper quality are real threats to causal interpretation but are not circularity; self-citations to the authors' prior bibliometric work are used only for dataset and venue selection and do not carry the argument. The score reflects one partial by-construction analysis while the central claim retains independent support.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim does not rest on invented entities or a fitted constant in the physics sense. Its burdens are the data-quality and model-specification assumptions above, plus the endogeneity of journal impact factors.

free parameters (3)
  • Average journal impact factors (CiteScore, SNIP, SJR) assigned as time-invariant = TSE: CiteScore 5.14, SNIP 4.12, SJR 1.39 (Table 8)
    Because yearly metrics were missing for many years, each journal received a single average score applied to all papers, which ignores within-journal temporal variation and can bias venue effect estimates.
  • Data exclusion thresholds for author disambiguation = author teams with over 800 past papers or over 200 past papers per author were manually checked; 26 merged author IDs…
    Hand-chosen thresholds determine how many papers are removed; 91 papers removed, which is small but could affect author-team variables for high-output teams.
  • Merging of paper types = Article, Article in Press, Conference Paper merged into 'Article'; Review and Short Survey into 'Review'; rest into…
    This grouping is a modeling choice that affects the estimated effects of paper type on citations.
assumptions (5)
  • domain assumption Scopus provides complete and accurate coverage of all SE journal papers and their citation counts.
    The dataset is entirely from Scopus; the authors note limitations but use Scopus as ground truth (Section 3.2.1).
  • domain assumption The 16 journals selected from two prior bibliometric studies constitute the population of SE journal papers.
    Section 3.2.2 defines the venue set; if major SE journals are omitted, the findings may not generalize to all SE papers.
  • domain assumption Omitted variables (paper quality, novelty, study design) are not confounders in the regression models.
    Section 3.1 explicitly excludes 'subjective' factors; the causal advice in Section 7 implicitly assumes these do not explain the observed associations.
  • ad hoc to paper The relationship between paper age and citations is adequately captured by log(age) and age terms.
    The authors chose this functional form over age and age-squared based on AIC (Section 3.1), a model-selection assumption specific to this paper.
  • standard math Negative binomial and quantile regression models estimated by R packages MASS and quantreg are correctly specified for citation count data.
    The paper relies on standard statistical properties of these models (Section 3.3).

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Cite this review

Pith. "Pith review of Citations in Software Engineering -- Paper-related, Journal-related, and Author-related Factors." pith.science (2026). https://pith.science/paper/NNH3ARSF

@misc{pith2026190804122,
  author       = {Pith},
  title        = {Pith review of: Citations in Software Engineering -- Paper-related, Journal-related, and Author-related Factors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NNH3ARSF}},
  note         = {Machine review of arXiv:1908.04122}
}
read the original abstract

Many factors could affect the number of citations to a paper. Citations have an important role in research policy and in measuring the excellence of research and researchers. This work is the first study in software engineering (SE) to assess multiple factors affecting the number of citations to SE papers. We use (a) negative binomial regression and (b) quantile regression to study arithmetic mean and median expected citations of a paper. Our dataset includes all the 25,113 papers which have been published in a set of 16 main SE journals, between 1970 and 2018. Our results indicate that publication venue, author team's past citations, paper length, the number of references, and the recency of references are the most influential factors on the number of citations to SE papers. From our empirical findings, we present several implications and advice to researchers for getting higher citations on their papers, which are in addition to the obvious case of conducting high-quality technical research, e.g. (1) Aim for high-profile venues, (2) Build a high-quality author team with highly cited past papers, and (3) Aim for high-quality work that has comprehensive content (thus longer paper length and reference list).

Figures

Figures reproduced from arXiv: 1908.04122 by the authors.

Figure 1
Figure 1. Number of software engineering journal papers published per year [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Citations per age (Age 1 means publication year 2018) [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Mean and median citations and fitted prediction lines from NB and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Citations per Journal (Note: Y-axis shows citations count [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Citations and 95% confidence interval by age with negative binomial [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

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Reference graph

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.