Enhancing Software Maintainability Prediction Using Multiple Linear Regression and Predictor Importance
Journal Title: International Journal of Experimental Research and Review - Year 2023, Vol 36, Issue 7
Abstract
Accurate maintenance effort and cost estimation are essential for effective software development. By identifying software modules with poor maintainability, Software Maintainability Prediction (SMP) plays a crucial role in managing software maintenance expenses. Previous research efforts have used multiple regression techniques to predict software maintainability, but the results regarding various accuracy and performance metrics are inconclusive. As such, developing a methodology that can recommend regression techniques for software maintainability prediction in the face of inconsistent performance or accuracy metrics is imperative. This research addresses the critical issue of software maintainability and presents a novel approach, the Software Maintainability Model (SMP) utilizing the Predictor Importance (PI) Method, Multiple Linear Regression (MLR), and five machine learning techniques. The proposed SMP integrates ten static source code metrics from object-oriented programming. MLR and PI implement feature selection, and the SMP's performance is evaluated based on accuracy and the Mean Magnitude of Relative Error (MMRE) parameters. Our findings are promising: for the User Interface Management System (UIMS) software, the proposed SMP demonstrates an impressive MMRE of 0.2441 and an accuracy of 91.91%. Similarly, for the Quality Evaluation System (QUES) software, an MMRE value of 0.2222 is achieved alongside a maximum accuracy of 80.95%. The ensemble method, when compared to other Machine Learning (ML) techniques, exhibits superior performance. These results affirm the effectiveness of our approach, contributing to the enhancement of software maintainability in object-oriented programming systems.
Authors and Affiliations
Rohit Yadav, Raghuraj Singh
Keywords
Related Articles
- EP ID EP726759
- DOI 10.52756/ijerr.2023.v36.013
- Views 39
- Downloads 0
Forecasting Wind Speed Using Clustering of Trend-Based Time Series Data
Accurate forecasting of wind speed is crucial for the efficient operation of wind energy systems. As a time-series concern, wind forecasting may help determine how much electricity a proposed wind farm might produce annu...
Dumdum airport: A necessity and luxury for human lifestyle but amenace for avian diversity
Pollution has always been one of the major outcome of human activities to improve their lifestyle. This often results in imbalance in the ecosystem of biological species which makes their survival questionable. Kolkata a...
Fabrication of microspheres and characterization of antimicrobial and anti-inflammatory activity isolated fraction from total alcoholic extract of Cassia Fistula (Linn.) in carrageenan-Type-IV induced inflammatory rats
The Cassia fistula Linn is known as the golden shower tree. It’s also referred to as a "disease killer" in Ayurvedic medicine. The current study investigates the effect and estimate of antimicrobial and Anti-inflammatory...
Identification of an antimicrobial peptide from large freshwater snail (Lymnaea stagnalis): activity against antibiotics resistant Staphylococcus epidermidis
Nowadays, antibiotic resistance in bacteria is a great public health problem of increasing magnitude due to quick evolution through mutation that has generated the urgency to find the effective...
Influence of chemical residue on the environmental engineering
This review summarizes waste management and there is a need for better understanding the scientific findings for recycling the waste materials and suggests areas where further research is needed. In major cities around t...