When producing good software, the quality of code exhibited during the process of coding plays a huge role in determining the end product. Sole developers, teams, and managers who are hired are expected to keep up certain simple disciplines and use dedicated tools where suitable to improve their code quality.
In this article, we are going to look at a few points a developer or one in charge of managing the end product would put into consideration to ensure good code quality.
First, we start by defining what good quality code is. If it can be read and understood at once, has minimal bugs, follows standard code rules, and successfully does what it has been built to do, then that code is of good quality.
Things like code reviews, tools, testing, management, code styles, and standards, among others, make the foundation for a developer to count on if he/she is thinking of an excellent product. For example, a software engineer manager (the management) responsible for checking the code quality could come up with organizational systematic measures to encourage the developers to maintain quality code.
Reviewing the code
Taking time to review code every after significant changes and features are added helps the developer in a way that the developer will quickly solve errors that would spoil the quality of code as well as saving him/her time and costs to maintain the program.
Having at least two people, including the writer of the code to go through it with the help of a method called the pull request. Pull request is one of the ways to review code where developers on platforms like Github upload their work in repositories where collaborators carry out a code quality analysis.
Reviewing the code creates awareness of whether it abides by the standard code rules/code style and also helps in cases where the team has to follow certain coding convention guidelines of the organization/software developing company.
If more time is spent reviewing code, it creates ample time to add more features, and less time will be spent fixing the remaining bugs at the final stages of the coding process. This also means less time will be required to maintain the program later.
After reviewing the code, the developer may engage in discussions to get ideas and advice on how the code could be made better.
Continuous integration is usually abbreviated as CI. It is where the code changes from multiple contributors (a team) working on the same software project are frequently automatically updated in a central repository on specific platforms.
This is done to enable developers on the team to easily identify errors in code and solve them immediately. Putting these pieces of code together to run them on say a daily basis gives a lot of feedback in time to avoid uncertainty at the time of deployment.
Tools like Jenkins, Circle CI, Gitlab CI, Codeship, Team City, Buddy, etc can be used to carry out the practice of Continuous Integration.
Analyzing and fixing bugs immediately
One would say the occurrence of bugs in code is probably unavoidable, which is true. However, timely code analysis to establish the impact of these bugs and what could have caused them is of an advantage to the developers, the management, and the entire organization at large.
Tracking bugs in code can also be done with the help of various tools like JIRA, Bugzilla, Mantis, Trac, Bug herd, etc. Once bugs have been fixed, it puts the developers in a better position where they will improvise measures to prevent the same mistakes from happening again, thus learning from their mistakes.
Tracking and measuring code metrics
Code metrics refers to a set of software measures that provide developers better insight into the code they are developing. These measures include; program vocabulary, program length, volume, the estimated number of bugs in a module, etc.
Duecode.io is one of the tools used in the practice of measuring code metrics. The tool acts as an analytics dashboard for code projects which aggregates historical git data in teams’ code repositories and can as well be used by an individual developer.
The tool presents the results of its code quality analysis in form of graphs. For example, a line graph monitoring whether/how the developer is refactoring code over time (rethinking the code as changes are made).
Massive copy-paste, which creates high chances of producing bad code and later increasing maintenance costs can also be detected using these code quality checking tools.
Using a code linter
A code linter reads code and outputs errors in form of warnings in circumstances where the code is not compliant with the standard of a language.
These errors and warnings may seem insignificant to a developer during the process of coding. However, as they pile up over time, they create a huge workload. And that is why it is advisable to pay attention to them and immediately find solutions.
Evaluating the code according to the standards maintains clean and steady coding progress, which leads to better code quality hence improving developer productivity.
For example, a developer programming in python could use Pylint to ensure that his/her code is matching the standard of the Python language as stated in the PEP 8 — Style Guide for Python Code.
Several projects may have their coding style guidelines, and in instances where these guidelines conflict with the convention of the standard language, the project-specific guides are considered.
Reading more books/articles published by experienced developers and participating in forums with topics about making code better could also be a better way to improve developer productivity in terms of good code quality.
Those are some of the ways code quality can be improved to ensure that the team’s workflow is generally aimed at having excellent software for the client/end-user.
This article was originally published at https://duecode.io/blog