Software Quality Review: JActor

Overall Software Quality Rating: High (from Maintainability & Usability perspective)

We reviewed JActor (http://sourceforge.net/projects/jactor/ ) in our software quality lab. Following is the overall structure of the software:

JActor Code Structure

The evaluation was done using manual and automated techniques. We were able to get findings related with following software quality characteristics:

Maintainability: As per the available data, this has been measured in terms of duplications, and rules compliance. Ideally, test coverage would also have helped to determine the testability of the code.

Our rating: High Maintainability

Refer to the data below retrieved from Sonar code analysis:

Duplications

 

Rules Compliance

 

Note the duplications value of 0.4%. This is outstanding. Also, there were no blocker or critical rules violations.

 Usability: This has been measured in terms of McCabe code complexity and documentation and rated accordingly.

 Our rating: High Usability

Code Complexity

Comments

 

 Note the code complexity to be very low in terms of methods as well as class. This indicates that code is easy to read, understand and learn. Also, the code has decent documentation of 48%.

 Overall rating: JActor can be rated as software of high quality keeping into account quality characteristics such as maintainability and usability. That said, we have not evaluated from functionality perspective and could not comment on that regard.

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Recent Posts

Agentic Reasoning Design Patterns in AI: Examples

In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents,…

1 month ago

LLMs for Adaptive Learning & Personalized Education

Adaptive learning helps in tailoring learning experiences to fit the unique needs of each student.…

1 month ago

Sparse Mixture of Experts (MoE) Models: Examples

With the increasing demand for more powerful machine learning (ML) systems that can handle diverse…

2 months ago

Anxiety Disorder Detection & Machine Learning Techniques

Anxiety is a common mental health condition that affects millions of people around the world.…

2 months ago

Confounder Features & Machine Learning Models: Examples

In machine learning, confounder features or variables can significantly affect the accuracy and validity of…

2 months ago

Credit Card Fraud Detection & Machine Learning

Last updated: 26 Sept, 2024 Credit card fraud detection is a major concern for credit…

2 months ago