software_dependencies — Software dependencies (2010)

Description

Class dependency networks of several Java software systems, constructed by L. Šubelj and collaborators from the source code. Nodes are software classes (including interfaces and nested classes) and a directed edge $i \to j$ indicates that class $i$ depends on (references) class $j$ (e.g. java.applet.Applet points to its superclass java.awt.Panel). The upstream files list one line per dependency, so a pair of classes can be connected by multiple parallel edges. The networks include all classes of the respective systems, also those without dependencies (e.g. 54 of the 6,488 classes of jdk are isolated).

• From Šubelj and Bajec (Physica A, 2011): javax, java and org (the corresponding namespaces of the Java SE Development Kit, JDK, version 1.6.0.7), colt (Colt 1.2.0, namespace cern; the same network, with the same class numbers and the same 3,691 dependencies, is distributed again with dependency types for Šubelj, Blagus and Bajec, NetSci 2013, and the types are included here), jung (JUNG 2.0.1, namespace edu.uci.ics.jung), flamingo (Flamingo 4.1), jmail (JavaMail 1.4.3) and junit (JUnit 4.8.1).
• From Šubelj and Bajec (SoftwareMining, 2012): jdk (the whole JDK 1.6.0.7), weka (Weka 3.6.6), jung-j (JUNG 2.0.1 together with the java and javax namespaces of the JDK) and jung-c (JUNG 2.0.1 together with Colt 1.2.0).
• guava: Guava r07 core libraries, where an edge indicates that the documentation of a class references another class.
• slucene (Lucene 4.1.0), from Šubelj, Blagus and Bajec (NetSci, 2013), and sjung (JUNG 2.0.1) and sjbullet (JBullet 2.72), from Šubelj et al. (Advs. Complex Syst., 2014).

The networks have the following properties:

• Nodes: name, the fully qualified name of the class (e.g. java.applet.Applet, with a dollar sign separating nested classes); not available for slucene, sjung and sjbullet, for which no class names are distributed upstream.
• Edges: type, only for slucene, colt, sjung and sjbullet, a letter code given upstream for each dependency (c, p, r, s, ...), presumably the type of dependency, whose meaning is not documented upstream.

Tags

Technological Software Unweighted Multigraph Metadata

Citation

• L. Šubelj, M. Bajec, "Community structure of complex software systems: Analysis and applications." Physica A 390(16), 2968-2975 (2011) 10.1016/j.physa.2011.03.036 Sci-Hub
• L. Šubelj, M. Bajec, "Software systems through complex networks science: Review, analysis and applications." Proc. 1st International Workshop on Software Mining (SoftwareMining 12), 9-16 (2012) 10.1145/2384416.2384418 Sci-Hub
• L. Šubelj, M. Bajec, "Clustering assortativity, communities and functional modules in real-world networks." arXiv:1202.3188 (2012) arXiv:1202.3188
• L. Šubelj, N. Blagus, M. Bajec, "Group extraction for real-world networks: The case of communities, modules, and hubs and spokes." Proc. of NetSci 13, 152 (2013) lovro.fri.uni-lj.si
• L. Šubelj, S. Žitnik, N. Blagus, M. Bajec, "Node mixing and group structure of complex software networks." Advs. Complex Syst. 17(7-8), 1450022 (2014) 10.1142/S0219525914500222 Sci-Hub

Upstream URL OK

http://wwwlovre.appspot.com/support.jsp

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$

Mean degree

$\sigma_k$

Standard deviation of the degrees

$\lambda_h$

Largest eigenvalue of the Hashimoto (non-backtracking) matrix

$\tau$

Random walk mixing time

$r$

Degree assortativity

$c$

Global clustering coefficient

$\oslash$

(Pseudo-) diameter

$S$

Relative size of the largest component

NPs

Node properties

EPs

Edge properties

Show all columns

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
jdk	6,488	150,985	23.27	479.26	129.28	15.74	-0.22	0.02	7	0.99	Directed	Unipartite			143 KiB	522 KiB	509 KiB	305 KiB
jung-j	6,126	138,706	22.64	423.69	128.35	15.74	-0.23	0.02	7	1.00	Directed	Unipartite			137 KiB	484 KiB	482 KiB	281 KiB
javax	3,310	22,370	6.76	59.13	42.52	418.89	-0.15	0.07	13	0.47	Directed	Unipartite			63 KiB	121 KiB	121 KiB	87 KiB
java	2,379	34,858	14.65	203.53	72.40	8.49	-0.30	0.03	5	1.00	Directed	Unipartite			52 KiB	138 KiB	133 KiB	85 KiB
slucene	3,471	17,512	5.05	32.76	34.35	173.27	-0.24	0.06	15	0.81	Directed	Unipartite			64 KiB	123 KiB	122 KiB	89 KiB
weka	2,124	10,000	4.71	51.49	35.09	562.89	-0.27	0.04	13	0.58	Directed	Unipartite			39 KiB	71 KiB	69 KiB	52 KiB
jung-c	1,040	5,363	5.16	37.38	16.46	576.87	-0.17	0.11	14	0.85	Directed	Unipartite			19 KiB	35 KiB	34 KiB	23 KiB
guava	838	3,088	3.68	24.91	12.06	769.76	-0.23	0.08	17	0.55	Directed	Unipartite			15 KiB	25 KiB	25 KiB	18 KiB
org	799	7,895	9.88	67.51	35.69	99.75	-0.26	0.14	5	0.61	Directed	Unipartite			16 KiB	36 KiB	34 KiB	24 KiB
colt	550	3,691	6.71	48.61	16.44	41.04	-0.26	0.11	8	0.92	Directed	Unipartite			10 KiB	19 KiB	19 KiB	12 KiB
jung	532	1,730	3.25	16.34	9.53	192.60	-0.21	0.10	11	0.75	Directed	Unipartite			10 KiB	16 KiB	16 KiB	12 KiB
sjung	533	1,735	3.26	16.34	9.53	192.26	-0.21	0.10	11	0.75	Directed	Unipartite			10 KiB	16 KiB	16 KiB	12 KiB
flamingo	388	846	2.18	8.63	8.82	93.93	-0.04	0.15	12	0.59	Directed	Unipartite			7 KiB	11 KiB	11 KiB	8 KiB
jmail	282	893	3.17	11.30	10.83	17.58	-0.05	0.22	9	0.68	Directed	Unipartite			6 KiB	9 KiB	9 KiB	7 KiB
sjbullet	309	1,742	5.64	23.30	13.12	15.89	-0.17	0.16	10	0.81	Directed	Unipartite			7 KiB	11 KiB	11 KiB	8 KiB
junit	181	470	2.60	9.54	10.74	36.48	-0.22	0.20	8	0.58	Directed	Unipartite			4 KiB	6 KiB	5 KiB	4 KiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.