Validating the Effectiveness of Object-Oriented Metrics for Predicting Maintainability

Kumar, Lov; Naik, Debendra Kumar; Rath, Santanu Kumar

doi:10.1016/j.procs.2015.07.479

Cited by 28 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Databases are obtained in two ways: constructing a new databases for the study or using ready databases. Databases and tools to extract metrics used in the literature are listed in Table II [8,10,14,17,18,22,23]. In Table 2, it is observed that projects that is used as dataset are written in Java or C++.…”

Section: Summary and Discussionmentioning

confidence: 99%

Software Code Maintainability : A Literature Review

Seref¹,

Tanriover²

2016

IJSEA

View full text Add to dashboard Cite

Software Maintainability is one of the most important quality attributes. To increase quality of a software, to manage software more efficient and to decrease cost of the software, maintainability, maintainability estimation and maintainability evaluation models have been proposed. However, the practical use of these models in software engineering tools and practice remained little due to their limitations or threats to validity. In this paper, results of our Literature Review about maintainability models, maintainability metrics and maintainability estimation are presented. Aim of this paper is providing a baseline for further searches and serving the needs of developers and customers.

show abstract

Section: Summary and Discussionmentioning

confidence: 99%

Software Code Maintainability : A Literature Review

Seref¹,

Tanriover²

2016

IJSEA

View full text Add to dashboard Cite

show abstract

“…A considerable amount of literature has investigated different types of software maintenance measurement: corrective maintenance effort [11]; adaptive maintenance effort [12]; maintainability index [13] and maintenance time [14]. A common maintenance effort measure used in many studies is based on changes made in the maintenance process and determines maintenance effort by computing the number of modifications made per class during the maintenance period [1][2][3][4][5][6][7][15][16][17]. A higher number of changes indicates greater maintenance effort.…”

Section: Related Workmentioning

confidence: 99%

“…A number of studies have employed individual machine learning models to predict software maintainability based on historical data of OO systems on the QUES dataset specifically: Bayesian network [1]; Multivariate adaptive regression splines [2]; TreeNet [3]; Mamdani-based model [4]; Group Method of Data Handling model [5]; Artificial neural network and genetic algorithm [6]; Neuro-Genetic algorithm [7]; Hybrid neural network and fuzzy logic approach [8]. Furthermore, two research studies have investigated the application of ensemble models in software maintainability: Aljamaan et al [15] developed a heterogeneous ensemble model by selecting the best base model in training; and Elish et al [17] evolved their work by generalizing a heterogeneous ensemble to include averaging, weighted averaging and best base in training as well.…”

Section: Related Workmentioning

confidence: 99%

“…Utilization of individual machine learning models has been investigated in several studies to predict software maintainability [1][2][3][4][5][6][7][8]. Recently, ensemble models have been applied across a wide range of software engineering problem domains, such as fault prediction, to increase accuracy prediction over individual models [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Software Maintainability in Object-Oriented Systems Using Ensemble Techniques

Alsolai

2018

2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)

View full text Add to dashboard Cite

Prediction of the maintainability of classes in object-oriented systems is a significant factor for software success, however it is a challenging task to achieve. To date, several machine learning models have been applied with variable results and no clear indication of which techniques are more appropriate. With the goal of achieving more consistent results, this paper presents the first set of results in an extensive empirical study designed to evaluate the capability of bagging models to increase accuracy prediction over individual models. The study compares two major machine learning based approaches for predicting software maintainability: individual models (regression tree, multilayer perceptron, knearest neighbors and m5rules), and an ensemble model (bagging) that are applied to the QUES data set. The results obtained from this study indicate that k-nearest neighbors model outperformed all other individual models. The bagging ensemble model improved accuracy prediction significantly over almost all individual models, and the bagging ensemble models with k-nearest neighbors as a base model achieved superior accurate prediction in both datasets. This paper also provides a description of the planned programme of research which aims to investigate the performance over various datasets of advanced (ensemble-based) machine learning models. Keywords-individual models, bagging ensemble model, software maintainability, prediction, Object-oriented systems.Fig. 1. Pred (.25) for each models apply on QUES dataset. Fig. 2. Box plot of MRE for models in QUES dataset.

show abstract

“…In the study done by Lov Kumara [15], empirically investigates the relationship of existing class level objectoriented metrics with a quality parameter i.e. maintainability.…”

Section: Related Workmentioning

confidence: 99%

An Evolving Neuro-PSO-based Software Maintainability Prediction

Chandrasekar¹

2018

IJCA

View full text Add to dashboard Cite

There are several issues related to the software maintenance but a more important critical one highlighted in this work is tracking over the behavior of software maintenance. This is because inferring the knowledge about the maintenance of software products in advance is really a difficult process which is pointed out by many researchers. Considering this issue the main purpose of this work is inspired based on BioInspirational behavior-based optimization technique with an objective to predict software maintainability. In this paper, an attempt has been made to use subset of class-level objectoriented metrics in order to predicting software maintainability. Here, different subset of Object-Oriented software metrics have been considered to provide requisite input data to design the models for predicting maintainability using Neuro-Particle Swarm Optimization algorithm (NPSO). This technique is applied to estimate maintainability on dataset collected from two different case studies such as Quality Evaluation System (QUES) and User Interface System (UIMS). The performance parameters used in this technique has been evaluated based on the basis of Magnitude of Relative Error (MRE), Mean Magnitude of Relative Error (MMRE) and Prediction.

show abstract

Validating the Effectiveness of Object-Oriented Metrics for Predicting Maintainability

Cited by 28 publications

References 20 publications

Software Code Maintainability : A Literature Review

Software Code Maintainability : A Literature Review

Predicting Software Maintainability in Object-Oriented Systems Using Ensemble Techniques

An Evolving Neuro-PSO-based Software Maintainability Prediction

Contact Info

Product

Resources

About