code_metrics
The purpose of this tool is to assess qualities of source code that may predict negative aspects such as entity coupling, cohesion, complexity, error-proneness, and overall maintainability. It is meant to be extensible via the addition of objects implementing new metrics.
This tool provides predicates for computing metrics for source files, entities, libraries, files, and directories. The actual availability of a particular predicate depends on the specific metric. One set of predicates prints, by default, the computed metric values to the standard output. A second set of predicates computes and returns a score (usually a compound term with the computed metric values as arguments).
API documentation
This tool API documentation is available at:
Loading
This tool can be loaded using the query:
| ?- logtalk_load(code_metrics(loader)).
Testing
To test this tool, load the tester.lgt
file:
| ?- logtalk_load(code_metrics(tester)).
Available metrics
Currently, the following metrics are provided:
Number of Clauses (
noc_metric
)Number of Rules (
nor_metric
)Unique Predicate Nodes (
upn_metric
)Cyclomatic Complexity (
cc_metric
)Depth of Inheritance (
dit_metric
)Efferent coupling, afferent coupling, instability, and abstractness (
coupling_metric
)Documentation (
doc_metric
)Source code size (
size_metric
)Halstead complexity (
halstead_metric
andhalstead_metric(Stroud)
)
A helper object, code_metrics
, is also provided allowing running all
loaded individual metrics. For code coverage metrics, see the
lgtunit
tool documentation.
Coupling metrics
Efferent coupling (
Ce
): Number of entities that an entity depends on. These include objects receiving messages from the entity plus the implemented protocols, imported categories, and extended/instantiated/specialized objects.Afferent coupling (
Ca
): Number of entities that depend on an entity. For a protocol, the number of protocols that extend it plus the number of objects and categories that implement it. For a category, the number of objects that import it. For an object, the number of categories and objects that send messages to it plus the number of objects that extend/instantiate/specialize it.Instability: Computed as
Ce / (Ce + Ca)
. Measures the entity resilience to change. Ranging from 0.0 to 1.0, with 0.0 indicating a maximally stable entity and 1.0 indicating a maximally unstable entity. Ideally, an entity is either maximally stable or maximally unstable.Abstractness: Computed as the ratio between the number of static predicates with scope directives without a local definition and the number of static predicates with scope directives. Measures the rigidity of an entity. Ranging from 0.0 to 1.0, with 0.0 indicating a fully concrete entity and 1.0 indicating a fully abstract entity.
The dependencies count include direct entity relations plus predicate calls or dynamic updates to predicates in external objects or categories.
For more information on the interpretation of the coupling metric scores, see e.g. the original paper by Robert Martin:
@inproceedings{citeulike:1579528,
author = "Martin, Robert",
booktitle = "Workshop Pragmatic and Theoretical Directions in Object-Oriented Software Metrics",
citeulike-article-id = 1579528,
citeulike-linkout-0 = "http://www.objectmentor.com/resources/articles/oodmetrc.pdf",
keywords = "diplomarbeit",
organization = "OOPSLA'94",
posted-at = "2007-08-21 11:08:44",
priority = 0,
title = "OO Design Quality Metrics - An Analysis of Dependencies",
url = "http://www.objectmentor.com/resources/articles/oodmetrc.pdf",
year = 1994
}
The coupling metric was also influenced by the metrics rating system in Microsoft Visual Studio and aims to eventually emulate the functionality of a maintainability index score.
Halstead metric
Predicates declared, user-defined, and called are interpreted as operators. Built-in predicates and built-in control constructs are ignored. Predicate arguments are abstracted, assumed distinct, and interpreted as operands. Note that this definition of operands is a significant deviation from the original definition, which used syntactic literals. A computation closer to the original definition of the metric would require switching to use the parser to collect information on syntactic literals, which would imply a much large computation cost.
The computation of this metric is parameterized by the Stroud coefficient for computing the time required to program (default is 18). The following individual measures are computed:
Number of distinct predicates (declared, defined, called, or updated;
Pn
).Number of predicate arguments (assumed distinct;
PAn
).Number of predicate calls/updates + number of clauses (
Cn
).Number of predicate call/update arguments + number of clause head arguments (
CAn
).Entity vocabulary (
EV
). Computed asEV = Pn + PAn
.Entity length (
EL
). Computed asEL = Cn + CAn
.Volume (
V
). Computed asV = EL * log2(EV)
.Difficulty (
D
). Computed asD = (Pn/2) * (CAn/An)
.Effort (
E
). Computed asE = D * V
.Time required to program (
T
). Computed asT = E/k
seconds (wherek
is the Stroud number; defaults to 18).Number of delivered bugs (
B
). Computed asB = V/3000
.
UPN metric
The Unique Predicate Nodes (UPN) metric is described in the following paper:
@article{MOORES199845,
title = "Applying Complexity Measures to Rule-Based Prolog Programs",
journal = "Journal of Systems and Software",
volume = "44",
number = "1",
pages = "45 - 52",
year = "1998",
issn = "0164-1212",
doi = "https://doi.org/10.1016/S0164-1212(98)10042-0",
url = "http://www.sciencedirect.com/science/article/pii/S0164121298100420",
author = "Trevor T Moores"
}
The nodes include called and updated predicates independently of where they are defined. It also includes multifile predicates contributed to other entities.
Cyclomatic complexity metric
The cyclomatic complexity metric evaluates an entity code complexity by measuring the number of linearly independent paths through the code. In its current implementation, all defined predicates that are not called or updated are counted as graph connected components (the reasoning being that these predicates can be considered entry points). The implementation uses the same predicate abstraction as the UPN metric. The defined predicates include multifile predicate definitions contributed by the entity to other entities.
For more details on this metric, see the original paper by Thomas J. McCabe:
@inproceedings{McCabe:1976:CM:800253.807712,
author = "McCabe, Thomas J.",
title = "A Complexity Measure",
booktitle = "Proceedings of the 2Nd International Conference on Software Engineering",
series = "ICSE '76",
year = 1976,
location = "San Francisco, California, USA",
pages = "407--",
url = "http://dl.acm.org/citation.cfm?id=800253.807712",
acmid = 807712,
publisher = "IEEE Computer Society Press",
address = "Los Alamitos, CA, USA",
keywords = "Basis, Complexity measure, Control flow, Decomposition, Graph theory, Independence, Linear, Modularization, Programming, Reduction, Software, Testing",
}
Usage
All metrics require the source code to be analyzed to be loaded with the
source_data
flag turned on. For usage examples, see the
SCRIPT.txt
file in the tool directory.
Be sure to fully understand the metrics individual meanings and any implementation limitations before using them to support any evaluation or decision process.
Excluding code from analysis
A set of options are available to specify code that should be excluded when applying code metrics:
exclude_directories(Directories)
list of directories to exclude (default is[]
); all sub-directories of the excluded directories are also excluded; directories may be listed by full or relative pathexclude_files(Files)
list of source files to exclude (default is[]
); files may be listed by full path or basename, with or without extensionexclude_libraries(Libraries)
list of libraries to exclude (default is[startup, scratch_directory]
)exclude_entities(Entities)
list of entities to exclude (default is[]
)
Defining new metrics
New metrics can be implemented by defining an object that imports the
code_metric
category and implements its score predicates. There is
also a code_metrics_utilities
category that defines useful
predicates for the definition of metrics.
Third-party tools
cloc
is an open-source command-line program that counts blank lines,
comment lines, and lines of source code in many programming languages
including Logtalk. Available at https://github.com/AlDanial/cloc
ohcount
is an open-source command-line program that counts blank
lines, comment lines, and lines of source code in many programming
languages including Logtalk. Available at
https://github.com/blackducksoftware/ohcount
tokei
is an open-source command-line program that counts blank
lines, comment lines, and lines of source code in many programming
languages including Logtalk. Available at
https://github.com/XAMPPRocky/tokei
Applying metrics to Prolog modules
Some of the metrics can also be applied to Prolog modules that Logtalk
is able to compile as objects. For example, if the Prolog module file is
named module.pl
, try:
| ?- logtalk_load(module, [source_data(on)]).
Due to the lack of standardization of module systems and the abundance of proprietary extensions, this solution is not expected to work for all cases.
Applying metrics to plain Prolog code
Some of the metrics can also be applied to plain Prolog code. For
example, if the Prolog file is named code.pl
, simply define an
object including its code:
:- object(code).
:- include('code.pl').
:- end_object.
Save the object to an e.g. code.lgt
file in the same directory as
the Prolog file and then load it in debug mode:
| ?- logtalk_load(code, [source_data(on)]).
In alternative, use the object_wrapper_hook
provided by the
hook_objects
library:
| ?- logtalk_load(hook_objects(loader)).
...
| ?- logtalk_load(code, [hook(object_wrapper_hook), source_data(on)]).
With either wrapping solution, pay special attention to any compilation warnings that may signal issues that could prevent the plain Prolog code of working when wrapped by an object.