Assessing Quality of Code from a Metric Perspective


The assessment of code through the use of different metrics is the key goal of supporting the maintenance and development of software. The assessment and analysis are used to automatically be able to make a prediction on the code faults, recommend refactoring, and change in proneness. However, most of the applications that have a strong link between the quality of the code as analyzed by the specified metrics and professed by the developers of codes. Taking an example, most of the refractory recommenders make use of the metrics whereby they act as the fitness function to classify the sequence of refactorings so as to enhance cohesion and reduce coupling. The last people who make use of the metric tools are the software developers, which means that all the code developers are supposed to be updated on the metrics that are able to capture coupling and cohesion as perceived by the developers (Khamis, 2010). According to the already done researches, it shows that there is not enough study done in this field.

The interviewed code developers are required to examine they’re apparent towards code coupling and good quality as accessed by the quality metrics. The assessment of the quality of code through perspective metrics gives the system developers the capability to determine the quality of the code and determine how to fix the faults. However, the quality of the code is a topic to be researched so as to tackle and also adopt new methods to investigate the quality of code such as readability, coupling, complexity, and cohesion. Nevertheless, to achieve the key objective in this assessment report, we used Github Java codes by mining over 200 commits.




Section 1

            The assessment and determination of the quality of code at a class level it is important that we consider the quality attribute classes and how they correspond to their respective metrics which can equally measure them. In the following attributes which expose the different qualities of code have automatic metric approaches that are selected form them specifically. Nevertheless, the perspective metrics that are implemented for the Java program.


Cohesion is the first quality attribute that is used in the assessment of the degree to which respective responsibilities on the implementation to which the class belongs to. When the cohesion is too high, it is desirable because it promotes and fosters the single responsibility principle hence hindering the maintainability of the code (García-Munoz, 2016). To carry the assessment of the cohesion attribute, we used two metrics to be able to access the classes.The first metric used was the Lack of Cohesion of Methods (LCOM), taking into consideration all the shared variables among the other methods that promote the cohesion of the class.Nevertheless, the other metric that was used is the Conceptual Cohesion of Classes (C3). This metric makes use of the Latent Semantic Indexing (LSI) to make the computation of the textual information in the classes of a program in defining the similarity index of the textual information.


Coupling is described as the strength of the dependences, which acts between the classes within the code. However, the low coupling is necessary because it helps in the isolation of changes. To enable the coupling in the codes, we used two types of metrics to ensure that all codes are analyzed. The first metric approach we used was the Coupling between Object (CBO), which helps in counting the number of dependencies in each of the classes within the code.As per the CBO metric, the higher the coupling between object, the higher the respective class of coupling. Nevertheless, the other metric which is used under this attribute is the Response for a Class (RFC), which is calculated as the number of all constructors and other distinctive approaches that are invoked by the classes within a code (Makkar, 2012). The Response for a class will be able to act as a proxy for complexity and also to the coupling.

Code complexity

One of the major objectives of developing code is to keep the complexity of the code low. For the accomplishment, this attribute goal to use the Weighted Method per Class metric to conduct the assessment of the class complexity. The Weighted Method per Class (WMC) used to calculate the summation of McCabe’s code complexity cycle and its other approaches. Nevertheless, it is worth static that the direction the WMC metric, the higher the class complexity of the code.

Number of Children (NOC)

A number of children are defined as the number of instantaneous code classes in the program protocol. However, the NOC depicts the high level of re-use. The amount of the number of children per class determines the abstraction level when the children per class are large, the abstraction level of the parent class is always reduced. Nevertheless, when the class of children is many, therefore it demonstrates the misuse of sub-classing. If a class has too many children, it may indicate misuse of sub-classing. Through this metric, the influence of one particular class is determined by the number of children. If a class has a large number of children, it may require more testing.


Section 2

To perform a clear and precise assessment of the codes, we had to use some of the automated tools. We selected to use the code cover tool so as to conduct the code coverage analysis as a way of evaluating the effectiveness of all the tests carried out in the Java Unit platforms. We will discuss the reasons as to why we chose to use the code coverage tools as part of the assessment. The following are some of the main reasons for which we choose the tools.

  • These tools are for both open-source and commercial development projects
  • These tools have a large market share compared with the other open-source coverage tools.
  • These have multiple report type format.
  • These tools are 100 % open-source

Section 3

Code Cover Tool

The Code Cover tool is an extensible open-source code tool that offers the provisions of quality and increases the test quality. This tool demonstrates the quality of tests conducted, which acts as a part of help to the development of new test cases. So we get a higher quality and better test productivity. However, the code cover has some characteristics which make it’s suitable as our choice such as loop coverage, performance of the source code implementation to obtain the most accurate coverage measures, branch coverage, the correlation matrix to locate the redundant test case which will help in optimizing the tests, supports the statement coverage, the source code must be highlighted as per the measured date. The environment at which the testing is conducted is used to set the tests for all the input programs, which are the JUnit3.


Section 4:Analysis and Discussion of results

Figure 1:  Box Plots of LCOM, C3, CBO, and RFC

Figure 2: Box PLot graph of WMC, B&w, and Spread


From the data we have been able to obtain from the respective metrics was used to plot the above graphs in the form of boxplots which have displayed the distribution of metrics used from the user code commits. Taking the comparison in the distribution of the C3 ns LCOM metrics for the classes on how they created an impact on the code commits demonstrating a way of improving the class cohesion. All the files that were analyzed from the Github commits are present in the data that was considered and focused on the modified files. However, we analyzed and made an assessment of the codesthrough the different metric distributions for both activities. When considering all files impacted in the commits we use the unpaired version of the test. Instead, when focusing only on modified files, we employ the paired version to increase the statistical power.

Finally, we qualitatively analyze interesting cases of agreement and disagreement between the improvements in quality as perceived by developers and as assessed by quality metrics. To this aim, one of the authors (from now on “inspector”) manually inspected all commits, looking at the code diff and at the metrics profile of the impacted code components before/after the commit. The goal was to identify commits suitable and interesting for qualitative analysis. With “suitable,” we refer to commits for which the inspector was able to fully understand the context of the commit, meaning the impacted code components.





From the start of this research, which was objected to assessing if the quality of the code as perceived by the software developers can be captured by the respective metrics. However, to test the aim of this study, we used over 1200 commits from Github through which the code quality could be improved through the four quality attributes such as coupling, readability, cohesion, and complexity of the code. Each of the commits of code used was determined through the quality of metrics, which was improved in the quality attribute to reflect on the improvement of metric values. We found that, more often than not, the considered quality metrics are not able to capture quality improvement as perceived by developers.


Khamis, N., Witte, R. and Rilling, J., 2010, June. Automatic quality assessment of source code comments: the JavadocMiner. International Conference on Application of Natural Language to Information Systems (pp. 68-79). Springer, Berlin, Heidelberg.

García-Munoz, J., García-Valls, M. and Escribano-Barreno, J., 2016.Improved metrics handling in SonarQube for software quality monitoring. In Distributed Computing and Artificial           Intelligence, 13th International Conference (pp. 463-470). Springer, Cham.

Wu, C.S., Chang, W.C. and Sethi, I.K., 2009, June.A metric-based multi-agent system for software project management.In 2009 Eighth IEEE/ACIS International Conference on          Computer and Information Science (pp. 3-8).IEEE.

Makkar, G., Chhabra, J.K. and Challa, R.K., 2012, March. Object-oriented inheritance metric reusability perspective.In 2012 International Conference on Computing, Electronics and         Electrical Technologies (ICCEET) (pp. 852-859).IEEE.

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