The document discusses the health of software packaging ecosystems, highlighting the challenges and consequences of dependency management within these ecosystems. It identifies issues such as security vulnerabilities, outdated or deprecated packages, and incidents like data breaches and typosquatting that can negatively impact developers and their projects. Furthermore, it emphasizes the importance of proper dependency management, updated practices, and adherence to semantic versioning to mitigate these risks and enhance ecosystem integrity.

1. Software Engineering Lab
Faculty of Sciences
Tom Mens
tom.mens@umons.ac.be
@tom_mens
Is my software ecosystem healthy?

2. Directed by Tom Mens
Department of Computer Science
Faculty of Sciences
tom.mens@umons.ac.be
http://informatique.umons.ac.be
Software Engineering Lab

3. SECO-ASSIST
"Excellence of Science”
Research Project
2018-2021
secoassist.github.io@secoassist

4. What is a software packaging ecosystem?
A collection of interdependent software packages
that are developed and distributed by
a large community of software developers
• Distributed development, e.g., through git
• Social coding (e.g., GitHub, GitLab, BitBucket)
• Package distribution through dedicated package managers
• Ecosystem-specific versioning and release policies
© 2019 Théo Zimmermann. Challenges in the collaborative evolution of a proof language and its ecosystem. PhD dissertation, Université de Paris

5. OS Package manager Logo
macOS MacPorts, Homebrew
Linux dpkg, apt, RPM, pacman
Windows winget, Windows Store, Chocolatey
Android Play Store
iOS App Store
ROS rospkg
Packaging ecosystems can be for
a specific operating system

6. Project #packages Logo
Eclipse >40M
Wordpress >67K
Atom >13K
Emacs >5K
…
Packaging ecosystems can be for
a specific (open source) project / community

7. Language Package manager #packages Logo
JavaScript npm >1.4M
PHP Packagist >0.33M
Python PyPI >0.26M
.NET NuGet >0.22M
Java Maven >0.19M
Ruby RubgyGems >0.16M
Cargo (Rust), CPAN (Perl), CRAN (R), NuGet (.NET), Hackage (Haskell), …
Packaging ecosystems can be for
a specific programming language

8. Libraries.io monitors 7,387,590 open source packages across 37 different
package managers
https://libraries.io (20 May 2020)

9. Packaging ecosystems can be unhealthy
• Bugs
• Security vulnerabilities
• Backward incompatibilities
• Single maintainer packages
• Abandoned packages
• Deprecated packages
• Outdated packages
• Bloated packages
• Micro-packages
• Non-compliance to versioning policies
• Incompatible or prohibited licenses
• Suboptimal release and update policies
• …

10. Dependency Hell
• Too many direct and transitive
dependencies
• Broken dependencies due to
backward incompatibilities
• Co-installability problems
• Deprecated dependencies
• Outdated dependencies
• Bloated dependencies

11. Case studies of
dependency-related
health issues in
software ecosystems

12. < short description of the reported health issue >
Symptom
< How was the health issue observed?
How did it impact the community or ecosystem? >
Diagnosis < What was the cause of the health issue? >
Cure < How was the health issue resolved? >
Prevention
< What could be done to prevent such health issues
to (re-)occur in the future? >
Case studies

13. data breach (May 2017)
“attackers entered its system in mid-May through a web-
application vulnerability (CVE-2017-5638) that had a patch
available in March. In other words, the credit-reporting giant had
more than two months to take precautions that would have
defended the personal data of 143 million people from being
exposed. It didn’t.”
Wired Magazine, “Equifax Has No Excuse”, September 2017

14. data breach (May 2017)
Symptom
From May to July 2017, a security vulnerability was exploited to
illegally obtain personal data from hundreds of millions of financial
customers.
Diagnosis
The vulnerability was coming from an out-of-date dependency
(Apache Struts).
Cure Apply the available security package of the problematic dependency.
Prevention
• Use vulnerability monitoring tools.
• Update dependencies if vulnerability fixes are available.

15. Typosquatting package names
(December 2018)
Two libraries were created by the same developer and mimicked other more
popular libraries using a technique called typosquatting to register similarly-
looking names. The first is "python3-dateutil," which imitated the popular
"dateutil" library. The second is "jeIlyfish" (the first L is an I), which mimicked
the "jellyfish" library. The jeIlyfish library had been available for nearly a
year, since December 11, 2018.
This is the sixth time the PyPI team intervenes to remove typo-squatted
malicious Python libraries from the official repository.
https://www.zdnet.com/article/two-malicious-python-libraries-removed-from-pypi/

16. Typosquatting package names
(December 2018)
Symptom
Packages stealing SSH and GPG keys from the projects of infected
developers.
Diagnosis
The packages use names that mimick other popular packages in the
registry.
Cure
The Python security team discovered and removed the packages.
(For one of them, only after a year.)
Prevention
• Be aware of depending on the right package.
• Defenses against typo squatting:
https://incolumitas.com/2016/06/08/typosquatting-package-managers/

17. “left-pad” incident (March 2016)

18. “left-pad” incident (March 2016)
Symptom
Significant downtime on major websites like
Facebook, Instagram, LinkedIn, Netflix.
Diagnosis
• Unexpected removal of left-pad package caused 5400 npm
packages to become uninstallable.
• Most of these packages only depended transitively on left-pad,
and were not even aware of its existence.
Cure
• Reintroduced older release of left-pad in the ecosystem.
• Changed ecosystem package removal policy.
Prevention
• Ecosystem-level: Prevent package releases from being removed
from the ecosystem.
• Package-level: Avoid having too many transitive dependencies.

19. “is-promise” incident (April 2020)
Symptom
Update 2.2.0 impacted millions of JavaScript projects (failed builds).
Update 2.2.1 failed to fix the problem.
Diagnosis
is-promise was directly used by 766 other npm packages.
Its update 2.2.0 did not adhere to proper ES module standards,
causing failed builds in its (transitive) dependents.
Cure Update 2.2.2 rolled back the changes after a couple of hours.
Prevention
• Benefit from automatic “same-day” fixes by using dependency
constraints that automatically accept patches.
• Avoid having too many direct (and transitive) dependencies.

20. “left-pad” and “is-promise” revisited

21. “left-pad” and “is-promise” revisited
Diagnosis
Too much dependence on very small “trivial” packages.
• left-pad contained only 11 lines of code
• is-promise contained only 2 lines of code
Cure Inline such overly simple code in your own package?
Prevention Avoid depending on trivial micro-packages.

22. “event-stream” vulnerability
(November 2018)
https://github.com/dominictarr/event-stream/issues/115
https://medium.com/@cnorthwood/todays-
javascript-trash-fire-and-pile-on-f3efcf8ac8c7

23. “event-stream” vulnerability
(November 2018)
Symptom An very popular npm package included code for stealing crypto-coins.
Diagnosis
• A developer volunteered to take over maintenance from the
original package developer.
• A dependency was added to flatmap-stream 0.1.0, which was then
modified in 0.1.1 to include Bitcoin-siphoning malware.
• All original dependents of event-stream were potentially at risk.
Cure
• Ecosystem took over ownership and removed vulnerable versions.
• Clients:
• Downgrade dependency on event-stream to older non-
vulnerable version, or upgrade to newer fixed version.
• Replace dependency on event-stream by another package.
Prevention
• Be aware of packages being taken over by new “untrusted”
maintainers.
https://blog.npmjs.org/post/180565383195/details-about-the-event-stream-incident

24. “request” deprecated (February 2020)

25. “request” deprecated (February 2020)

26. “request” deprecated (February 2020)
Symptom
In March 2019, request was announced to “go into maintenance
mode and stop considering new features or major releases.”
https://github.com/request/request/issues/3142
In February 2020, request is fully deprecated.
Diagnosis
In August 2020, still >55 thousand packages and >5.5 million GitHub
repositories depend on request!
Cure Replace dependencies on request by alternative packages.
Prevention
Provide better tool support for:
• warning dependents of deprecated packages;
• supporting migration strategies to alternatives.

27. “request” revisited
Increasing complexity of transitive dependency graph
https://npm.anvaka.com/

28. https://npm.anvaka.com/
“request” revisited
Increasing complexity of transitive dependency graph

29. https://npm.anvaka.com/
“request” revisited
Increasing complexity of transitive dependency graph

30. https://npm.anvaka.com/
“request” revisited
Increasing complexity of transitive dependency graph

31. https://npm.anvaka.com/
“request” revisited
Increasing complexity of transitive dependency graph

32. Empirical Research on
dependency-related
health issues in
packaging ecosystems

33. Empirical research on packaging ecosystems
Why do developers use trivial packages? An empirical case study on npm
• R Abdalkareem et al., ESEC/FSE 2017
Do developers update their library dependencies? An empirical study on the impact of
security advisories on library migration
• R G Kula et al. Empirical Software Engineering Journal, 2017
On the impact of security vulnerabilities in the npm package dependency network
• A Decan et al., MSR 2018
On the evolution of technical lag in the npm package dependency network
• A Decan et al., ICSME 2018
An empirical study of dependency downgrades in the npm ecosystem
• F R Cogo et al., IEEE Transactions on Software Engineering, 2019

34. Empirical research on packaging ecosystems
What do package dependencies tell us about semantic versioning?
• A Decan et al., IEEE Transactions on Software Engineering, 2019
An empirical comparison of dependency network evolution in seven software
packaging ecosystems
• A Decan et al., Empirical Software Engineering Journal, 2019
An empirical study of same-day releases of popular packages in the npm ecosystem
• F R Cogo et al. Empirical Software Engineering Journal, 2020 (under review)
A comprehensive study of bloated dependencies in the Maven ecosystem
• C Soto-Valero et al. Empirical Software Engineering Journal, 2020 (under review)
Deprecation of packages and releases in software ecosystems: A case study on npm
• F Cogo et al., IEEE Transactions on Software Engineering, 2020 (under review)

35. Characterising the evolution of
software packaging ecosystems
Fast package dependency
network growth in two years
Packaging
ecosystem
#packages
(2018-01)
#packages
(2020-01)
% growth #deps
(2018-01)
#deps
(2020-01)
% growth
npm 630K 1.218K 93% 19.0M 48.7M 156%
RubyGems 141K 180K 28% 1.92M 2.40M 25%
Packagist 121K 155K 28% 2.17M 4.73M 118%
Cargo 13K 35K 169% 257K 796K 210%

36. Characterising the evolution of
software packaging ecosystems
830K packages – 5.8M package versions – 20.5M dependencies (April 2017)
An Empirical Comparison of Dependency Network Evolution
in Seven Software Packaging Ecosystems
A Decan, T. Mens, Ph. Grosjean (2019) Empirical Software Engineering 24(1)

37. Characterising the evolution of
software packaging ecosystems
Package dependency networks grow exponentially in
terms of number of packages and/or dependencies
Fastest growth for npm
Slowest growth for CRAN

38. Characterising the evolution of
software packaging ecosystems
Continuing change
• Number of package updates grows over time
• >50% of package releases are updated within 2 months
• Required and young packages are updated more frequently
Fastest growth for npm
Slowest growth for CRAN

39. Impact of transitive dependencies
Incidents with left-pad, is-promise
and request affected thousands of
transitively dependent packages!
Number of packages that are transitively
required by at least 5% of all packages.
Unhealthy packages may have a very high transitive impact.

40. Impact of transitive dependencies
Transitive dependency depth
of top-level packages
Over 50% of top-level
packages have a deep
dependency graph
Health issues may be difficult to detect
due to transitive dependencies
• Most dependency monitoring tools
do not support transitive dependencies

41. Trivial Packages
• Trivial packages implement simple and trivial tasks
Cf. leftpad and “is-promise” case study
• Trivial packages are prominent
They make up 16.8% of 230K studied packages
• Developers perceive trivial packages as well implemented and well-tested
• In reality, less than half of all trivial packages have tests!
Why do developers use trivial packages? An empirical case study on npm.
R Abdalkareem, O. Nourry, et al. (2017) ESEC/FSE conference

42. Deprecated packages
• 54% of all packages transitively
depend on at least one deprecated
package release.
• In more than half of the cases,
dependency depth is 4 or higher.
Deprecation of packages and releases in software ecosystems: A case study on npm.
F Cogo, G Oliva, A Hassan (2020) IEEE Transactions on Software Engineering (under review)
• npminstall tool warns about deprecated packages, but does not signal where
they can be found in the dependency tree
• When a transitive package is deprecated, the client package has no control
over the migration to a replacement release.

43. Outdated Dependencies
Should package maintainers upgrade their
dependencies to more recent versions?
�Upgrades benefit from bug and security fixes
� Upgrading allows to use new features
� Upgrading requires effort
� Upgrading may introduce breaking changes

44. Outdated Dependencies
npm outdated checks which dependencies can be updated
Can check for transitive dependencies, up to a preset maximum
depth level (with --depth argument)

45. Outdated Dependencies
How do dependency constraints play a role?
• Implicit updates will install newer versions of dependencies
without needing to change the version constraint
• Explicit updates require replacing the version constraint to be
able to update the dependency
major minor patch
3 9 2
Most
permissive
Most
Restrictive

46. Outdated Dependencies
• 1 out of 3 packages never update their dependency
• Outdatedness is related to the type of dependency constraint
being used
Strict constraints represent about 20% of all dependencies,
but about 33% of all outdated dependencies
All
runtime dependencies
Outdated
runtime dependencies
On the evolution of technical lag in the npm package dependency network.
A Decan, T Mens, E Constantinou (2018) Int’l Conf. Software Maintenance and Evolution

47. Semantic Versioning
https://semver.org
major minor patch
3 9 2
Breaking
changes
Backwards
compatible
changes
Bug fixes
recommended to respect
semantic versioning

48. Semantic Versioning
By making dependency constraints “semver-compliant”, the
proportion of outdated releases could be reduced by >17%
“What if” analysis:
What do package dependencies tell us of semantic versioning?
A Decan, T Mens (2019) IEEE Transactions on Software Engineering

49. Semantic versioning
Different packaging ecosystems interpret
version constraints in different ways
More restrictive than semver
More permissive than semver

50. Semantic versioning
To which extent do software packaging ecosystems
enable/adhere to semantic versioning?
All considered ecosystems become more semver-compliant over time.
mostly semver-compliant
More than 16% of the constraints are restrictive, preventing
automatic adoption of backward compatible upgrades

51. Semantic Versioning
Wisdom of the Crowds
Maintainers of required packages should
leverage test suites of dependent packages to discover
whether a new release is likely to be backward-incompatible.
Using Others' Tests to Identify Breaking Updates.
S Mujahid, R Abdalkareem, et al. (2020) IEEE Int’l Conf. Mining Software Repositories
What do package dependencies tell us of semantic versioning?
A Decan, T Mens (2019) IEEE Transactions on Software Engineering
Maintainers of dependent packages should
look at how other packages depend on a required package
to decide which version constraint to use.

52. Semantic Versioning
Semantic versioning reduces outdatedness
Ecosystem compliance to semver increases over time
- Trust is important
Not all provider packages respect semver
 risk of unexpected breaking changes
• Avoid dependency constraints that are too permissive
or too restrictive
• When using implicit dependencies on semver-
compliant packages, write automated CI tests for the
functionalities you use from those packages
• Use “wisdom of the crowds”: to verify semver-
compliance; to discover breaking releases

53. Technical Lag
Technical lag measures how outdated a package or dependency
is w.r.t. the “ideal” situation
where “ideal” = “most recent”;
“most secure”;
“least bugs”;
…
A formal framework for measuring technical lag in component repositories
– and its application to npm
A Zerouali, T Mens, J Gonzalez-Barahona, et al. (2019) J. Software Evolution and Process
On the evolution of technical lag in the npm package dependency network.
A Decan, T Mens, E Constantinou (2018) IEEE Int’l Conf. Software Maintenance and Evolution

54. Technical Lag - Example
1.1.0
dependent
package
required
package p 1.1.3
tech-lag(p) = delta(p, ideal(p))
for any required package p
dependency

55. Technical Lag - Example
Time-based measurement
(ideal = most recent release; delta = time difference)
1.0.0 2.0.01.1.0 1.1.1 2.0.1
Time lag
date(1.1.3) - date(1.1.0)
1.0.1 1.1.2 1.1.3
dependent
package
required
package p

56. Technical Lag - Example
Version-based measurement
(ideal = highest release; delta = version difference)
1 major 1 patch
Version lag
1 major + 1 patch
required
package p
1.0.0 1.1.01.0.1
dependent
package
2.0.01.1.1 2.0.11.1.2 1.1.3

57. Technical Lag - Example
Vulnerability-based measurement
(ideal = least vulnerable release; delta = #vulnerabilities)
Security lag
less vulnerable and more recent
required
package p
1.0.0 1.1.01.0.1 2.0.01.1.1 2.0.11.1.2 1.1.3
dependent
package

58. Technical Lag - Example
Bug-based measurement
(ideal = least known bugs; delta = #known bugs)
Bug lag
less bugs
required
package p
dependent
package
1.0.0 1.1.01.0.1 2.0.01.1.1 2.0.11.1.2 1.1.3
Dependency (constraint) needs to be
downgraded to reduce bug lag …
~1.1.0

59. Technical Lag - Example
Bug lag
required
package p
dependent
package
1.0.0 1.1.01.0.1 2.0.01.1.1 2.0.11.1.2 1.1.3
“Fixing” dependency constraint
downgrades too far …
=1.1.0
An empirical study of dependency downgrades in the npm ecosystem.
F Roseiro Côgo, G Ansaldi Oliva, A E Hassan (2019) IEEE Transactions on Software Engineering
This happens in 13% of all downgraded releases…

60. Technical Lag
Quantitatively assesses the risk/benefit of package updates
Along multiple dimensions (time, version, bugs, vulnerabilities, …)
• Be aware of your outdatedness
• Avoid depending on packages that are too outdated
(perhaps deprecated or unmaintained?)
• Use (and improve) tools to measure and reduce lag,
e.g. through automated updates

62. Unmaintained Packages
Bus Factor: The risk of a package becoming unmaintained if
(some of) its core developers leave development
On the abandonment and survival of open source projects:
An empirical investigation.
G Avelino, E Constantinou, MT Valente, A Serebrenik (2019) ESEM conference

63. Unmaintained Packages
Bus Factor
• Slows down development
• Increases risk of bugs and vulnerabilities
• That propagate (transitively) to dependent packages
• Leads to unmaintained packages if no replacement is found
• Increases risk of “hostile takeovers” by malevolent developers
• Cf. npm event-stream vulnerability case study

64. Unmaintained Packages
Challenges in the collaborative evolution of a proof language and its ecosystem.
Théo Zimmerman (2019) Université de Paris
Two out of three non-trivial packages on GitHub
are likely to be single-maintainer packages

65. Unmaintained Packages
How to predict future inactivity of package developers?
GAP: Forecasting commit activity in git projects
A Decan, E Constantinou, T Mens, H Rocha (2020) Journal on Systems and Software
pip install git+https://github.com/AlexandreDecan/gap
Based on a probalistic model
of future days of activity

66. Unmaintained Packages
Unmaintained packages increase risk of becoming outdated
and vulnerable
This risk is ecosystem-wide, due to the strongly connected
transitive network of package dependencies
• Avoid depending on unmaintained packages
• Use (and improve) tools to detect and avoid developer
abandonment
• Rely on “community organizations” of volunteers willing to
“steward” and maintain important abandoned packages
A first look at an emerging model of community organizations for the
long-term maintenance of ecosystems' packages.
Théo Zimmermann (2020) ICSE Workshop on Software Health

67. How to increase health?
Recommendations for package maintainers
• Inform your dependents about
• Incompatible upgrades (cf. semantic versioning)
• Planned updates
• Deprecated features or releases
• Known bugs and security issues
• Limit the number of direct and transitive dependencies
• Remove any unused dependencies
• Write automated tests for the functionality you use from your
dependencies
• Monitor and improve your dependencies for
• outdated packages
• unmaintained packages
• security issues and bugs
• micro-packages
• deprecated packages

68. Conclusion
Is my software ecosystem healthy?
It probably isn’t, and it probably never will be.
• Depending on unhealthy packages impact the whole
ecosystem due to its dense package dependency network
• Communities and package maintainers should be aware
and vigilant.
• Trust is important, but so is being cautious.
• Monitoring tools and policies can help a long way but are
no silver bullet.
• More research is needed

69. Related Research
More precise package dependency analysis
at code level (e.g. function calls) to improve
accuracy