A Look at the Dynamics of the
JavaScript Package Ecosystem
Erik Wittern Philippe Suter Shriram Rajagopalan
IBM T. J. Watson Research Center
{witternj, psuter, shriram}@us.ibm.com
Abstract
The node package manager (npm) serves as the fron-
tend to a large repository of JavaScript-based software
packages, which foster the development of currently
huge amounts of server-side Node.js and client-side
JavaScript applications. In a span of 6 years since its in-
ception, npm has grown to become one of the largest soft-
ware ecosystems, hosting more than 230, 000 packages,
with hundreds of millions of package installations every
week. In this paper, we examine the npm ecosystem from
two complementary perspectives: 1) we look at package
descriptions, the dependencies among them, and down-
load metrics, and 2) we look at the use of npm packages
in publicly available applications hosted on GitHub. In
both perspectives, we consider historical data, providing
us with a unique view on the evolution of the ecosys-
tem. We present analyses that provide insights into the
ecosystem’s growth and activity, into conflicting mea-
sures of package popularity, and into the adoption of
package versions over time. These insights help under-
stand the evolution of npm, design better package recom-
mendation engines, and can help developers understand
how their packages are being used.
1 Introduction
Software ecosystems consist of software projects that
are developed and evolve together in a shared envi-
ronment [13]. Research on such ecosystems only re-
cently emerged within software engineering [15]. It ad-
dresses, among other things, the analysis of the char-
acteristics and evolution of software ecosystems, which
some researchers consider to be a central subject in this
field of research [26]. To that regard, some software
ecosystems have been scientifically assessed, including
the Maven [23], Apache [2], Gentoo [4], Ruby [11], and
R [8] ecosystems. Nonetheless, a systematic literature
study from 2013 found that research regarding real-world
software ecosystems is lacking [14].
The study of characteristics and the evolution of soft-
ware ecosystems is an end in itself, allowing to under-
stand how certain technologies spread or why others fail.
In addition, this research can also inform the design of
new ecosystems and associated tooling, including tech-
nical as well as social aspects [26]. Furthermore, as soft-
ware projects are seldom created in isolation, studying
individual software projects often requires studying the
ecosystem around them [3].
The node package manager (npm) combines a set of
open source tools that developers use to describe their
JavaScript packages, and specifically Node.js packages.
These tools include, for example, a command line inter-
face to create and maintain package.json files, which
declare, among other things, the name, description, ver-
sion, and dependencies of a package. npm furthermore
features a registry that developers can use to publish their
packages, making them available for others to use. Pack-
ages within npm may depend on each other, and software
projects outside of npm, for example applications, may
specify dependencies to packages hosted on npm. Since
its creation in 2009, npm has grown rapidly to now fea-
ture over 230, 000 packages (as of January 28th, 2016).
npm provides a complete set of various historic data
points on the packages in the ecosystem. This data thus
not only provides insights into the current state of the
ecosystem, but also into how the ecosystem evolved over
time. The diversity of available data points furthermore
allows us to assess the ecosystem from multiple perspec-
tives and to compare these perspectives. Understand-
ing npm provides valuable insights into the rapid growth
JavaScript and Node.js experienced within the last years.
It also helps to understand how individual packages rose
in popularity, prevailed, and sometimes were eventually
replaced or disregarded.
This paper presents an extensive analysis of the npm
ecosystem. We make the following contributions:
• We study the evolution of the npm ecosystem re-
1
garding growth and development activities. Our
findings indicate a highly active developer commu-
nity. We look at the relationships across packages
and find that package dependencies have increased
from 23.4% in 2011 to 81.3% in 2015, with 32.5%
of packages having 6 or more dependencies. On
the other hand, only 27.5% of packages in the npm
ecosystem are being depended upon, indicating that
developers largely depend on a core set of packages.
• We assess the notion of package popularity in npm.
Our analysis considers three different popularity
measures and finds that they are not substitutes for
another. Rather, they can be used to depict the pop-
ularity of specific types of packages. These find-
ings impact the design of package recommendation
tools. We further assess the evolution of package
popularity for one selected measure, focusing on
top ranking packages as well as on sets of functional
equivalent ones. We further find that new packages
continuously enter the top ranks for the first time,
while there are also selected packages that manage
to remain popular over time.
• We study the creation of new package versions in
npm, and the adoption of package versions by appli-
cation developers. We find that package maintain-
ers adopt a variety of numbering conventions and
that version numbers in themselves are therefore not
good predictors of package maturity. We observe
that application developers are flexible when declar-
ing dependencies, and often automatically accept
minor updates. A detailed analysis of usage data
for the popular express package shows that, as a
consequence, up to half of all users automatically
depend on the latest version when it is released.
The rest of this paper is organized as follows: we
present the data at the foundation of our analyses, and
how we collected it (Section 2). We then present our
findings regarding the evolution npm (Section 3), pack-
age popularity (Section 4), and version adoption (Sec-
tion 5). We give an overview of related work (Section 6)
before concluding (Section 7).
2 Data Sources
We confine our observation period from October 1
st
2010
to September 1
st
2015, and all data collected for our
analyses was pruned to cover this range only.
1
We
collected three data sets that provide indicators about
the evolution and popularity of packages, from different
sources such as the npm registry, GHTorrent [9] and the
1
Despite npm being created in 2009, we were able to obtain reliable data
about packages only starting from October 1
st
2010.
GitHub project hosting platform. Our final dataset in-
cludes 185, 005 npm packages and 114, 995 applications.
The following subsections provide further details on our
collection process.
2.1 Package Metadata
The metadata associated with a package contains infor-
mation such as the dependencies on other packages, ver-
sion information, search keywords associated with the
package, as well as download (i.e. installation) counts.
We use this data in Section 3 to analyze the relationship
between packages, in Section 4 to analyze the popularity
of packages, and in Section 5 to analyze the evolution of
versions over a package’s lifetime.
1 {
2 "name": "myexample",
3 "version": "1.3.1",
4 "maintainers": [
5 {"name": "Some Name",
6 "email": "me@example.org"}
7 ],
8 "repository": {
9 "type": "git",
10 "url": "https://github.com/x/myexample"
11 },
12 "main" : "myexample.js",
13 "keywords": ["Web", "REST"],
14 "dependencies": {
15 "async": "~0.8.0",
16 "express": "4.2.x"
17 },
18 "devDependencies": {
19 "vows": "0.7.0",
20 "assume": ">=2.5.2 <3.0.0"
21 }
22 }
Listing 1: npm metadata file package.json for a ficti-
tious package myexample. Some fields are omitted for
brevity.
Metadata files. Each npm package has an associated
metadata file called package.json as exemplary shown
in Listing 1. We obtained the metadata files correspond-
ing to every version of every package during the obser-
vation period. This data is publicly available from the
npm registry
2
. Every package is uniquely identified by
the name field (line 2). The package.json file con-
tains version information (line 3) specified using seman-
tic versioning [22], the source repository associated with
the package (line 8), keywords associated with the pack-
age (line 13), and information about developers main-
taining the package (line 4). npm distinguishes between
two types of dependencies: dependencies (line 14) spec-
ifies the set of runtime dependencies, and devDependen-
cies (line 18) specifies the set of modules required by the
2
source: https://registry.npmjs.org/-/all
2
package developer for her development and testing pur-
poses.
Download counts. Every time a package is installed
from npm (whether it is for production, testing or devel-
opment), its download count is incremented. npm pub-
lishes the download data on a daily basis through its
web API
3
. We obtained the download figures for each
package in npm (irrespective of its version) for every day
within the observation period.
2.2 Applications using npm Packages
To obtain a sample of Node.js-based applications that
use npm packages, we turned to open source project
hosting platforms. Specifically, for the purposes of this
study, we targeted various types of applications and tools
hosted on the popular GitHub platform. We use the in-
formation collected from these applications in Section 4
to assess the popularity of packages as determined by
their usage, and in Section 5, to measure adoption ratios
of specific package versions.
Since GitHub hosts software projects in different lan-
guages, we first needed to obtain a list of GitHub
projects written in JavaScript. For this purpose, we an-
alyzed the data set from the GHTorrent [9] project, ob-
tained in March 2015. The dataset contained information
on 245, 389 JavaScript projects from GitHub.
4
We then eliminated to the extent possible npm pack-
ages that are themselves hosted on GitHub and that
may thus be contained in the GHTorrent dataset. The
package.json file described earlier optionally includes
a repository field (Listing 1, line 8) with a link to the
source repository. We used this information to filter out
npm packages from our application dataset, leaving us
with a list of 237, 349 JavaScript projects.
We further refined our application dataset to include
only software projects specifying dependencies to npm
(typically but not exclusively Node.js projects); using
GitHub’s web API, we identified such projects by look-
ing for the existence of a package.json file. Our fi-
nal list of GitHub applications using npm packages con-
sisted of 114, 995 software projects. For each project in
this final list, we cloned its repository and analyzed the
commit history pertaining to the package.json file, to
retrieve every different version together with the times-
tamp at which it was committed. We obtained a total of
4, 222, 864 versioned files, indicating that on average a
project had 36.7 commits affecting package.json (the
median was 12, the maximum 1, 203).
3
source: https://api.npmjs.org/downloads/range/2010-01-01:
2015-09-01/packageName
4
Note that the date at which this dataset was obtained has no bearing on
our observation window. We used this dataset to simply obtain a sample set of
JavaScript-based projects in GitHub.
2011 2012 2013 2014 2015
0
5000
10000
15000
20000
25000
No. of Packages
New Packages
Updated Packages
Figure 1: Packages created per month and packages up-
dated per month. Packages with multiple updates in a
month are counted only once.
While the number of projects in our GitHub data set
may be a small subset of the total number of JavaScript-
based projects in GitHub that use npm packages, we be-
lieve that we have a representative sample set for our
analyses. Looking at this sample set, we found that the
oldest date at which projects in this set were either cre-
ated or first updated dated as far back as March 2010,
even earlier than our observation window.
3 Ecosystem Evolution
The npm package repository was created in 2009. Over
the last six years, the software repository has evolved
rapidly and currently hosts over 230, 000 packages. We
investigate the evolution of this ecosystem over this pe-
riod and look for signs of stagnation. Stagnation indi-
cates that the community involvement has slowed down,
while continued signs of growth and activity indicates
that there is increasing adoption and contributions by the
developer community. To characterize growth and ac-
tivity, we look at the number of new packages added
to the repository over the observation period, the num-
ber of packages that were updated, and the dependencies
among packages.
In Figure 1, we show the growth in number of pack-
ages that are being added to npm every month, and the
number of packages that are being updated per month.
Broadly speaking, we find that the developer community
around Node.js has been steadily increasing over the
last 6 years, as evidenced by the increasing number of
packages being created every month in the npm reposi-
tory. In addition, the community is also quite active in
terms of maintaining the packages hosted in the reposi-
tory, as indicated by the two-fold increase in the number
of packages being updated every month.
3
2011 2012 2013 2014 2015
0
200000
400000
600000
800000
1000000
No. of Packages / Dependencies
0
2
4
6
8
10
Dependencies per Package
Figure 2: The y1-axis on the left shows the growth in
the number of packages in npm (blue line) and the de-
pendencies among packages (green line) over time. The
y2-axis on the right shows the average number of de-
pendencies per package (red line) over time.
We then compare the growth in the total number of
packages over time with the dependencies, i.e., relation-
ships, between packages. The dependency measure per
package is the sum of number of its dependencies men-
tioned in dependencies and devDependencies fields
in the package.json file corresponding to each pack-
age. As shown in Figure 2, not only is the npm ecosystem
growing superlinearly in terms of the number of pack-
ages and dependencies, the relationship among packages
is also growing at a much higher rate, indicating that the
packages are depending more and more on each other.
We confirm our observation by plotting the average num-
ber of dependencies per package (red line) in the same
figure. On average, a package in npm had approximately
4-6 dependencies on other packages in late 2015, com-
pared to just one dependency in early 2011.
To further understand the dependency relationship be-
tween packages, we constructed a directed graph, where
packages form the vertices and directed edges between
vertices represent the dependency between the two pack-
ages. The out degree of a vertex indicates the number of
dependencies of the package represented by the vertex,
while the in degree represents the number of packages
that depend on the given package.
Figure 3 shows the distribution of the out degrees
across all packages over time. The number of packages
having one or more dependencies has increased from
23.4% in January 2011 to 81.3% by end of August 2015.
Specifically, there has been a steady increase in the num-
ber of packages with 6 or more dependencies, starting
with 0% in January 2011 and reaching 32.5% by end of
August 2015.
Given the increasing number of external dependencies
2011 2012 2013 2014 2015
0
10
20
30
40
50
60
70
80
90
100
Percentage of Packages in npm
0
1
2
3
4
5
6 or more
Figure 3: Characterizing packages in npm by their depen-
dencies on other packages.
per package, we tried to understand whether such depen-
dencies were spread equally across all packages or if they
were confined to just a limited set of packages. To an-
swer this question, we look at Figure 4, showing the dis-
tribution of in degrees across all packages. At the end
of August 2015, 72.5% of packages had no incoming de-
pendency, i.e., they had no dependent packages, while
only 4.9% of packages had 6 or more dependents, up
from 1.1% in January 2011. Such uneven distribution of
package dependencies has been previously observed in
other software ecosystems as well [18]. In Section 4, we
investigate the ranking of packages within and outside
the ecosystem to shed light on the dynamics of package
popularity in the npm ecosystem.
Takeaways. We find that the npm ecosystem continues
to grow in terms of the number of packages it hosts.
At the same time, the number of packages being up-
dated monthly has also grown two-fold, indicating that
the developer community remains quite active in terms of
maintaining their packages. Looking at the relationships
between packages, we find there is an increasing amount
of dependency across packages, with 81.3% of them de-
pending on at least one package and 32.5% of them de-
pending on 6 or more packages. However, the proportion
of packages that are being depended upon is only 27.5%
of overall packages in npm, indicating that package de-
pendencies exhibit a power law distribution, as has been
observed by prior research on software-related artifacts
and ecosystems [18, 12].
4 Package popularity
In this section, we analyze the popularity of packages in
the npm ecosystem and the evolution of package popular-
ity over time. Popularity may be a function of different
4
2011 2012 2013 2014 2015
0
10
20
30
40
50
60
70
80
90
100
Percentage of Packages in npm
0
1
2
3
4
5
6 or more
Figure 4: Characterizing packages in npm by the number
of packages depending on them.
measures, either individual ones or combinations. In our
analysis we focus on the following three measures:
1. The npm rank reflects the PageRank [6] of a pack-
age within the npm dependency graph. Packages
specify dependency relationships to each other as
described in Section 2.1. By applying the iterative
PageRank algorithm on the resulting dependency
graph, a package obtains a high PageRank value if
it is depended upon by many packages that them-
selves have high PageRank values. Ordering pack-
ages by their PageRank value, we assign the result-
ing npm rank to them. The npm rank thus denotes
the relative importance of every package within
npm. PageRank is commonly used to rank soft-
ware artifacts, for example Java components [10]
or JavaScript packages [21]. In our measure for the
npm rank, we consider both the dependencies and
development dependencies that a package specifies.
When performing the PageRank algorithm, we ap-
plied a damping factor of 0.85 and stopped itera-
tions once the total cumulative change in the values
of all vertices was below 10
−6
.
2. The download rank reflects the number of times a
package was downloaded within one month leading
to the considered date. As described in Section 2.1,
the download figures are published on a daily basis
per package by npm and were thus aggregated by us.
By ordering packages by their download figures, we
derive the download rank.
3. The GitHub rank reflects, for a given day, the num-
ber of dependencies on a package as stated in the
GitHub projects we collected (c.f. Section 2.2). As
in the case of download numbers, we derive a rank-
ing of packages from ordering the counts.
npm pagerank Downloads GitHub
npm pagerank - 0.385 0.445
Downloads 0.385 - 0.567
GitHub 0.445 0.567 -
Table 1: Spearman rank correlation coefficients between
package popularity measures.
Unless specified explicitly, all measures of popularity
were computed based on data as of September 1
st
2015.
Popularity measures can be used for package recom-
mendation, or source code recommendation more gen-
erally, which is a common goal of recommendation sys-
tems in software engineering [7]. In many existing sys-
tems, like npm’s own search interface [19], the “npm
Discover” tool [20], or the “npm packages PageRank”
tool [21], users enter search terms to specify require-
ments and thus narrow down the packages to consider.
The remaining packages are then ranked for users based
on a single popularity measure or a combination of
them. For example, “npm packages PageRank” relies on
PageRank values, while “npm Discover” considers usage
from GitHub projects.
4.1 Relationships between Measures
A first question to answer is whether the considered mea-
sures report the popularity of a package in a consistent
way. To this end, we calculated the Spearman’s rank cor-
relation coefficients between them as illustrated in Ta-
ble 1. The input data used to calculate the correlations
only covers a subset of all packages: The npm rank for
packages that are not depended upon at all cannot be
determined because they all share the same, minimum
PageRank value. Similarly, the GitHub rank of pack-
ages that are never depended upon cannot be determined.
Thus, when calculating the correlation coefficients in Ta-
ble 1, we consider only packages with assigned ranks for
both of these measures. All packages featured at least
one download, so we did not have to dismiss any pack-
age based on this measure. The low correlation values
presented in Table 1 show that the three popularity mea-
sures do not generally depict popularity in the same way
and can thus not necessarily be substituted for another.
To assess the relationship between the measures in
more detail, Figure 5 plots the differences in ranks of
packages for every combination of popularity measures.
Again, for every comparison, we consider only packages
with assigned ranks in both measures. As a consequence,
each comparison is done over a different number of pack-
ages. The y axis in each of the top three graphs in Fig-
ure 5 ranges from minus to plus the number of compared
packages. Limiting the axes this way allows the three
5
graphs to be compared to each other with regards to the
shape of the distribution. The three histograms at the
bottom of Figure 5 illustrate the distribution of the dif-
ferences in popularity measures of packages.
As can be seen, all three comparisons result in a rel-
atively normal distribution of differences in popularity
ranks. On the one hand, this result may seem predictable,
given the large size of data points we considered. On
the other hand, all comparisons exhibit extreme cases
where packages rank considerably higher in one measure
as compared to the other and vice versa.
Takeaways. Different package popularity measures pro-
duce different outcomes. All comparisons of the three
measures considered in this work reveal packages that
perform strongly in the first measure and poorly in the
second as well as packages for which the opposite is true.
This finding has implications on package recommenda-
tion tools making use of PageRank within npm, e.g., [21].
While their recommendations may be useful for package
developers, they might not be suited for application de-
velopers.
4.2 Differentiating Package Types
Section 4.1 revealed that there are packages with signif-
icantly different ranks regarding the different popularity
measures. To gain insight into the nature of these pack-
ages, we now focus on two measures, npm rank and the
GitHub rank. We dismiss download ranks because we
cannot with certainty explain their origin or exclude in-
fluences, for example, through web miners or crawlers.
Focusing on the npm rank and GitHub rank, we pro-
pose to explain their differences by defining the follow-
ing types of packages:
• End user packages are used commonly in applica-
tions, but not necessarily by other packages. Exam-
ples are database drivers like bookshelf (GitHub
rank: 399, npm rank: 2950), or authentication
libraries like passport (GitHub rank: 65, npm
rank: 718). We expect end-user packages have
high GitHub ranks, but a comparatively low npm
ranks. Given that many recommendation systems
filter down packages based on user-input, these ex-
emplary differences in rank can make the difference
between a package being displayed in the top re-
sults or not. For example, among all packages in
npm with the keyword “authentication” assigned,
passport ranks 1st based on the GitHub rank, but
only ranks 3rd based on the npm rank.
• Core utility packages are mostly used by other
packages but seldom by applications outside of
npm. Examples are packages providing low-level
functionalities like ieee754 (GitHub rank: 37287,
npm rank: 2258) for reading/writing floating point
numbers to buffers or is-relative (GitHub rank:
20299, npm rank: 434) for detecting relative pack-
age dependencies. We expect core utility packages
have high npm ranks, but low GitHub ranks.
In order to assess whether we find evidence for this
classification of packages, we look further into the na-
ture of packages with highly different ranks. Packages
in npm can be categorized by any number of keywords,
which package developers may assign, as shown in List-
ing 1, line 13. We assess the keywords assigned to the
1000 packages with the highest npm and GitHub rank.
We count the appearances of every observed keyword
and calculate the Pearson correlation coefficient between
these counts. The resulting correlation coefficient of
0.823 is relatively strong.
Thus, to look into more detail, we focus our analysis
on those packages that reveal the highest difference in
npm rank as compared to the GitHub rank. Table 2 shows
the keywords with the highest difference in count in “npm
strong” packages as compared to “GitHub strong” pack-
ages. “npm strong” denotes the set of the 1000 pack-
ages that perform comparatively the best in npm while
performing the worst in GitHub. On the other hand,
“GitHub strong” denotes the set of 1000 packages that
perform comparatively the best in GitHub while per-
forming the worst in npm. As we can see, the keywords
most unilaterally used to describe “npm strong” packages
relate to low-level capabilities such as dealing with ar-
rays, buffers, or strings. These keywords are assigned to
core utility packages, as introduced above.
In contrast, Table 3 shows the opposite, that is, the
keywords with the highest different in count in “GitHub
strong” packages as compared to “npm strong” packages.
As we can see, the keywords most unilaterally used to
describe “GitHub strong” packages are related to capa-
bilities typically used in application development. grunt
and gulp are plug-in-supporting tools to build applica-
tions. express is a server-side web application frame-
work, and react is a library used to render views. These
keywords are assigned to user packages, as introduced
above.
Takeaways. We assumed that there are qualitative differ-
ences between packages with either high npm ranks and
low GitHub ranks or vice versa. Our analysis of the key-
words used uniquely to describe these packages confirms
this suspicion. We find indications for both core utility
packages and end user packages. This finding strength-
ens our above takeaway that package recommendation
requires choosing an appropriate popularity measure de-
pending on the intended outcome.
6
0 5000 10000 15000 20000
Package
−20000
−10000
0
10000
20000
Diff. npm pagerank
and GitHub rank
0 10000 20000 30000 40000
Package
−40000
−20000
0
20000
40000
Diff. npm pagerank
and download rank
0 10000 20000 30000
Package
−30000
−20000
−10000
0
10000
20000
30000
Diff. GitHub rank
and download rank
−20000 0 20000
Diff. npm pagerank
and GitHub rank
0
2000
4000
6000
8000
10000
Frequency
−40000 0 40000
Diff. npm pagerank
and download rank
0
5000
10000
15000
20000
Frequency
−30000 0 30000
Diff. GitHub rank
and download rank
0
5000
10000
15000
Frequency
Figure 5: Differences in ranks between popularity measures.
Keyword “npm strong” “GitHub strong” Diff.
util 35 3 32
array 18 3 15
buffer 16 2 14
string 20 6 14
file 21 7 14
Table 2: Count of keywords ordered by their difference
in count between describing “npm strong” and “GitHub
strong” packages.
Keyword “GitHub strong” “npm strong” Diff.
gruntplugin 92 24 68
gulpplugin 54 9 45
express 34 5 29
react 31 2 29
authentication 22 1 21
Table 3: Count of keywords ordered by their difference
in count between describing “GitHub strong” and “npm
strong” packages.
4.3 Evolution of Popularity
Having established differences in the meaning of differ-
ent popularity measures, we now focus on one measure
and assess how package popularity evolves with regard to
it over time. The npm rank denotes how central a pack-
age is to the npm ecosystem. To obtain npm ranks over
time, we start with the complete dependency graph as of
September 1
st
2015. Using the date annotations between
all edges in the graph, we then create a filtered version of
that graph for every week between September 1
st
2010
and September 1
st
2015. For every one of the resulting
257 graphs, we calculate the PagerRank value of every
package present at that point in time and assign the npm
rank thereupon.
4.3.1 Identifying Top Packages
An immediate question to answer is which packages per-
form the best over the whole existence of npm. One way
to answer this question is, as illustrated in Figure 6, to
determine the packages with the lowest mean npm rank
(i.e., the highest ranked packages). We use the geomet-
ric mean for this purpose as it is less prone to outliers as
compared to the arithmetic mean. We limit the y axis in
Figure 6 from 1 to 100 for readability.
The top packages illustrated in Figure 6 are diverse
in nature. should and nodeunit are tools for testing,
uglify-js is used to minimize and obfuscate (client)
code, coffee-script is a language compiling down to
JavaScript, and underscore provides a set of generic
utility functions.
While the packages presented in Figure 6 stand for
long running success, a more fine grained analysis is
needed to gain insights into momentary package success.
Figure 7 breaks down the 5 packages with the lowest
mean npm rank per year from 2011 to 2015. We see that,
for example, coffee-script ranks in the top 5 only in
2011 and 2012. Other packages, like the file system util-
ity glob or the evolution of testing tool tape (built on
top of tap) only make it into the top 5 in later years,
i.e., 2014 and 2015. Interestingly though, the npm rank
of packages in the top 5 remains relatively stable, espe-
cially from 2013 on.
7
2011 2012 2013 2014 2015
1
10
20
30
40
50
60
70
80
90
Pagerank
mocha
should
tap
uglify-js
coffee-script
Figure 6: npm ranks (PageRanks) over time of the 5 pack-
ages whose npm ranks have the lowest geometric mean.
4.3.2 Top Package Dynamics
As there seems to be little dynamics in the yearly top
5 packages as shown in Figure 7, we aim to determine
how many packages manage to enter top npm ranks over
time. Table 4 depicts the overall number of packages en-
tering top 10, top 100, and top 250 npm ranks per year.
While Table 4 indicates declining numbers of new pack-
ages entering top ranks, it also shows that there is still a
considerable amount of them, even in 2015.
Year Top 10 Top 100 Top 250
2011 15 180 445
2012 5 46 142
2013 4 36 116
2014 2 44 99
2015 3 27 60
Table 4: Number of packages per year entering top npm
ranks for the first time.
4.3.3 Comparing Popularity of Similar Packages
Another capability arising from being able to determine
npm ranks over time is to compare selected packages
against each other. Figure 8 shows the npm ranks of
selected utility packages over time. These utility pack-
ages typically provide a broad set of capabilities like im-
proving convenience in dealing with data types like ob-
jects, arrays, or strings. As Figure 8 shows, one package
in particular, underscore, has been introduced early in
npm’s history
5
. Since its release, underscore held a top
5
It first ranks at January 14
th
2010 in our data. However, its earliest commit
on GitHub stems from October 25
th
2009 and a first package.json was added in
version 0.2.0, which was released on October 28
th
2009, c.f. https://github.
com/jashkenas/underscore/commits/master
npm rank. Nonetheless, multiple competitors have en-
tered npm since then, some of which directly position
themselves as alternatives to underscore. For exam-
ple, lodash evolved from a fork from the underscore
project and kept API compatibility
6
, and lazy.js pro-
claims to be “[...] similar to underscore and lodash,
[...]”
7
. The selection of presented utility packages is
based on a web search for underscore competitors.
While various underscore competitors have entered
npm throughout its history, most of their npm ranks are
on a declining trajectory since 2014, even if they exhib-
ited growth before. The one exception is lodash, which
has gradually risen in npm rank since its introduction and
was able to surpass underscore for good in May 2015
according to our data.
Takeaways. Calculating npm ranks (i.e., PageRanks)
over time allows to identify well-performing packages
across the life-cycle of a software ecosystem. They can
also be used to determine how dynamic or static the ranks
of the most popular packages are. For npm, we find that,
while decreasing, there is still considerable amount of
change in the top ranks. Nonetheless, comparing func-
tionally similar packages indicates that high popularity
for some packages may be long lasting. In our example,
we find that most utility libraries are declining as com-
pared to the dominant underscore, except competitor
lodash which positioned itself well by providing API
compatibility.
5 Version Numbering & Adoption
While the previous sections treat each npm package as a
single entity, in this section we consider some questions
that arise from studying the diversity and evolution of
version numbers of given packages, as well as the usage
of package versions by application developers.
As in most software repositories, npm artifacts (pack-
ages) are versioned to indicate their evolution and to let
developers rely on older or newer features as desired. By
convention, version numbers follow the semantic ver-
sioning format [22]: three dot-separated numbers indi-
cating, respectively, the major, minor, and patch versions
of an artifact.
Semantic version numbers are lexicographically or-
dered, i.e. version m
1
.n
1
.p
1
> m
2
.n
2
.p
2
if and only
if m
1
> m
2
∨ (m
1
= m
2
∧ n
1
> n
2
) ∨ (m
1
= m
2
∧ n
1
=
n
2
∧ p
1
> p
2
). In order to visually present data where one
axis represents a spectrum of version numbers, we often
need to convert these triples into a single value. Because
none of the version components have upper bounds, we
cannot simply consider them to be fractional parts with
6
http://kitcambridge.be/blog/say-hello-to-lo-dash/
7
c.f. http://danieltao.com/lazy.js/
8
Jan 2011
Feb 2011
Mar 2011
Apr 2011
May 2011
Jun 2011
Jul 2011
Aug 2011
Sep 2011
Oct 2011
Nov 2011
Dec 2011
1
3
5
7
9
11
Pagerank
expresso
vows
underscore
coffee-script
eyes
Jan 2012
Feb 2012
Mar 2012
Apr 2012
May 2012
Jun 2012
Jul 2012
Aug 2012
Sep 2012
Oct 2012
Nov 2012
Dec 2012
1
3
5
7
9
11
mocha
should
tap
uglify-js
coffee-script
Jan 2013
Feb 2013
Mar 2013
Apr 2013
May 2013
Jun 2013
Jul 2013
Aug 2013
Sep 2013
Oct 2013
Nov 2013
Dec 2013
1
3
5
7
9
11
mocha
tap
should
uglify-js
coffee-script
Jan 2014
Feb 2014
Mar 2014
Apr 2014
May 2014
Jun 2014
Jul 2014
Aug 2014
Sep 2014
Oct 2014
Nov 2014
Dec 2014
1
3
5
7
9
11
mocha
tap
tape
should
uglify-js
Jan 2015
Feb 2015
Mar 2015
Apr 2015
May 2015
Jun 2015
Jul 2015
Aug 2015
Sep 2015
Oct 2015
Nov 2015
Dec 2015
1
3
5
7
9
11
mocha
tap
tape
should
glob
Figure 7: npm rank (PageRank) over time of 5 packages whose npm ranks have the lowest geometric mean per year.
2011 2012 2013 2014 2015
1
1000
2000
3000
4000
5000
6000
7000
8000
9000
Pagerank
underscore
lodash
lazy.js
sugar
valentine
wu
Figure 8: npm rank (PageRank) over time of selected util-
ity packages.
a fixed denominator. Instead, to combine a principal
and secondary component (e.g. minor and patch), we
sum them up, applying to the secondary one a mapping
f : [0, +∞[→ [0, 1[. For this paper, we chose the function:
f (x) = 1 −
1
k · x + 1
with k =
1
2
as a scaling factor.
8
In order to produce a
single rational number from a semantic version triple, we
apply the function twice:
r(m.n. p) = m + f (n + f (p)) (1)
With these preliminary considerations in mind, we
first look at how package maintainers work with ver-
sions.
5.1 Attribution of Version Numbers
The first question we look at is the distribution of version
numbers across all npm packages. Figure 9 displays this
8
The choice of any positive k is somewhat arbitrary. We picked this value to
appropriately spread out the version numbers we observed.
0.0.0
1.0.0
2.0.0
3.0.0
4.0.0
5.0.0
6.0.0
7.0.0
8.0.0
9.0.0
10.0.0
Version number
10
0
10
1
10
2
10
3
10
4
10
5
Frequency
Figure 9: Frequency of version numbers (using (1))
across all npm packages.
for all versions less than 10.0.0 (which adds up to 95% of
all packages). As one could expect from a rapidly grow-
ing ecosystem, low version numbers dominate not only
at the major level, but also within each major number.
This is confirmed by looking at the frequencies of each
version component in isolation (Figure 10); lower num-
bers are always more common, with the exception of the
minor version number 9 which is more common than 8.
A possible explanation is that developers would use a mi-
nor version of 9 to indicate that the next major release is
“almost there”, even when not all minor numbers have
been used. Also not pictured in Figure 10 is the major
version number 2014, which is overall the 8
th
most com-
mon, indicative of an ad-hoc convention of numbering
versions by the year.
Having seen that small values dominate all compo-
nents of the package version numbers, we can try to es-
tablish to which extent this is due to the relative young
age of the ecosystem; we do this by comparing pack-
age version numbers to their age as they are released.
We define as the age of package the time since its first
9
0 1 2 3 4 5 6 7 8 9
Number
10
2
10
3
10
4
10
5
10
6
Frequency
Figure 10: Frequency of individual version components
for npm packages; blue, red and green denote major, mi-
nor, and patch numbers respectively.
version was released. Figure 11 shows, for all version
numbers for which at least 100 packages exist, the aver-
age package age at the time of release. The graph shows
a combination of trends: generally, higher version num-
bers come later in the development of a package. How-
ever, we also observe trends within major version num-
bers. In fact, the graph shows that on average, it takes
about a year for a package to reach either version 0.9.0
or version 6.1.0. We interpret this as a manifestation that
package authors adopt numbering schemes that may not
be strictly in accordance to the semantic versioning prin-
ciple; a large number of package authors are reluctant for
instance to ever release a version 1.0.0.
Takeaways. Although npm package authors are encour-
aged to follow the semantic versioning scheme, other
numbering conventions have emerged, resulting for in-
stance in a large set of pre-1.0.0 packages, irrespective
of their age.
5.2 Adoption by Version Number
We now look at how developers declare dependencies on
package versions, based on the data we collected from a
large set of open-source applications hosted on GitHub
(see Section 2.2). Dependencies on versions in npm can
be declared using queries built with a variety of opera-
tors; the simplest cases are fixed, explicit, dependencies,
where a project author requests a specific version of a
package, but the author can also request, for instance, any
version with a fixed major component, any version with
a fixed minor component, any version within a range,
the most recent version in general, etc.
9
Using histori-
cal package release and project evolution data, we have
9
See http://semver.npmjs.com.
0.0.0
1.0.0
2.0.0
3.0.0
4.0.0
5.0.0
6.0.0
7.0.0
Version number
0
2
4
6
8
10
12
Months
Figure 11: Average time in months after the initial
release to reach a given version number (using (1)).
Dataset limited to version numbers reached by at least
100 packages. Blue, red, and green denote major, minor,
and patch versions respectively.
Query Rel. freq. Avg. # vs.
* 1× 7.59
^n.n.n / n.x.x 7.28× 2.36
~n.n.n / n.n.x 17.53× 1.66
n.n.n 10.73× 1.0
Table 5: Relative popularity of version dependency
query types, from most permissive to most restrictive,
and average number of versions to which the queries
resolve within the time between two dependency up-
dates. Data aggregated from approximately 4M version
updates.
the ability to retroactively resolve these requirements and
know precisely which version of a package would have
been returned for which project at any time.
Towards this goal, we processed the 4M+ versions of
package.json we obtained and recorded for each de-
pendency the points in time at which the version query
changed. This gave us 3, 955, 338 version update points,
indicating that on average, when package.json is up-
dated (which happens every 90 days on average), 0.93
dependencies are updated. Table 5 shows the relative fre-
quencies for selected types of queries, namely requesting
the latest version, allowing patch or minor updates, al-
lowing patch updates, and requesting an exact version.
The table also displays the average number of versions
to which queries resolve over their life time.
For each dependency query, we then computed the set
of all possible versions it can have resolved to. We obtain
10
this set by intersecting the time intervals of the updates
to package.json with the release dates of the packages.
We find that on average, within the lifetime of a commit,
a package query will resolve to 1.88 different versions.
Finally, our data also allows us to answer a ques-
tion that package developers may find crucial as they
issue releases; given that many package consumers use
flexible queries, what is the fraction that will obtain a
new version when it is released, without changing their
package.json. We call this measure “implicit adoption
ratio”, and obtain it by computing, for each package ver-
sion at its release date, the size of the set of projects re-
solving to the latest version divided by the size of the
set of projects resolving to any of the versions. Fig-
ure 12 shows the implicit adoption trends for the popular
express package for building web applications. Note
that the second part of the graph indicates that releases
are continually issued both for the 3.x.x and 4.x .x ver-
sion families. From it we get several insights: first, patch
versions have higher implicit adoptions ratios than mi-
nor versions, which have higher ratios than the two ma-
jor versions visible in the chart. This is explained by
the tendency to adopt version queries which minimize
incompatible updates. Second, as new releases come out
in the 4.x.x, the implicit adoption ratio increases, indi-
cating that the fraction of projects configured to accept
these new releases grows over time. Finally and as a
complement to the second observation, the fraction of
projects implicitly resolving to the latest version in the
3.x.x family shrinks gradually and decisively over time.
The last two points can be explained either by a combi-
nation of 1) the continuously growing number of projects
using express, which tend to use the latest version when
they are created (not visible on the graph), and 2) exist-
ing express projects that migrate to 4.x.x series when
they can afford to.
Takeaways. Through declaring dependencies with
queries, application developers can benefit from auto-
mated upgrades, at various levels of granularity. This
mechanism is used widely in practice, and new releases,
particularly patch ones, have high implicit immediate
adoption ratios.
6 Related Work
Empirical analysis of software ecosystems is an im-
portant aspect of software ecosystem research as a
whole [26]. Correspondingly, related work focuses on
specific aspects like visualization [13], depicting ecosys-
tem maturity [1], or how to aggregate software quality
metrics [17].
Some works empirically analyze software ecosys-
tems that evolve around a specific programming lan-
guages, as we did for npm. Raemaekers et al. present a
crawled dataset containing basic metrics, dependencies,
and changes with some aggregate statistics about Maven,
a popular package manager for Java [23]. Another
work runs software to identify bugs in source code of li-
braries shared in the same ecosystem [16]. In contrast to
these works, our study of the npm ecosystem focuses on
the ecosystem evolution, popularity measures, and pack-
age versioning. An analysis of the statistical computing
project R [8] finds a super-linear growth in packages as
we report in Section 3. In addition, the study focuses
on characterizing contributions to user-contributed ver-
sus core packages. We refrain from running a similar
analysis as npm does not differentiate packages explic-
itly in such a way, although we did identify different
types of packages based on our analysis of popularity
measures (see Section 4.2). In [11], the authors present
results of a quantitative study of the Ruby ecosystem.
The paper presents a graph visualization of the whole
ecosystem as well as some descriptive statistics and his-
tograms about selected characteristics of packages, in-
cluding downloads and package size. In contrast to our
work, the dataset is much smaller, having only around
10K gem nodes and 13.1K dependencies. Furthermore,
the paper does not go into the dynamics of the ecosystem,
considering instead a single point in time. We did not
find any published empirical analyses of the npm ecosys-
tem.
Some works have studied the evolution of versions and
corresponding change of software projects. For example,
in a recent empirical study [5] regarding two ecosystems
(including npm) the authors find that developers struggle
with changing versions as they might break dependent
code. Similar assessments on the effects of changes have
been made regarding the Apache ecosystem [2] or the
Maven ecosystem [24]. In contrast to these works, we
assess versions in npm from a black-box perspective: we
do not assess how version changes are reflected in the
implementation of individual packages, but focus on the
occurrence of version numbers and how they are adopted
by application developers.
Finally, npm has occasionally been analyzed out of the
context of peer-reviewed venues. npm packages pager-
ank provides a keyword-based search for packages, and
presents the results as recommendations based on their
PageRank [21]. While we also consider the PageRank as
a possible popularity measure, we have shown that this
metric may not be adequate for packages most useful to
application developers (Section 4.2). The project npm
by numbers analyses a snapshot of the npm ecosystem
from September 2015 and presents various statistics on
it, including the distribution of version numbers and re-
leases of packages and the dependencies between pack-
ages [25]. In contrast to our work, npm by numbers con-
11
Figure 12: Implicit adoption of new releases of the express package. A circle indicates a new release, where blue,
red, and green indicates whether it is a major, minor, or patch release. The diameter of the circle denotes the fraction
of applications that immediately resolved to the new version as it was released. The circles denote values ranging from
2% to 48%.
siders only a single point in time, whereas we focus on
the evolution of the ecosystem, and provides no insight
derived from client applications.
7 Conclusion
In this paper, we conducted an analysis of the npm
ecosystem, one of the largest software ecosystems en-
compassing application frameworks, libraries, and utili-
ties used in both server-side Node.js and browser-side
JavaScript applications. We find npm to be a striving
ecosystem with ongoing and even accelerating growth
of packages and increasing dependencies between them.
Our findings regarding the differences in popularity mea-
sures can be used to improve the search and recommen-
dation systems targeting npm, as well as help developers
to make informed decisions when choosing packages for
use in their applications. Finally, our assessment of ver-
sion numbers indicates different conventions embraced
by developers, despite the prescribed usage of seman-
tic versioning, and our assessment of version adoption
shows that flexible version queries can lead to significant
immediate adoption ratios.
12
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