.. _library_code_metrics:

`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. A 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:

`../../docs/library_index.html#code-metrics <../../docs/library_index.html#code-metrics>`__

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 and halstead_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 an 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 includes 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 larger 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 as EV = Pn + PAn.
Entity length (EL). Computed as EL = Cn + CAn.
Volume (V). Computed as V = EL * log2(EV).
Difficulty (D). Computed as D = (Pn/2) * (CAn/An).
Effort (E). Computed as E = D * V.
Time required to program (T). Computed as T = E/k seconds (where k is the Stroud number; defaults to 18).
Number of delivered bugs (B). Computed as B = 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 is 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 path
| exclude_files(Files) | list of source files to exclude (default is []); files may be listed by full path or basename, with or without extension
| exclude_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

The following open-source command-line programs can count blank lines, comment lines, and lines of source code in many programming languages, including Logtalk:

cloc - https://github.com/AlDanial/cloc
ohcount - https://github.com/blackducksoftware/ohcount
tokei - 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 from working when wrapped by an object.