A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques

Prasad, Chituri; Florence, Lilly; Arya, Arti

doi:10.14257/ijdta.2015.8.3.15

Cited by 45 publications

(29 citation statements)

References 35 publications

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“…Moreover, the studies in [11], [12] discussed various ML techniques and provided the ML capabilities in software defect prediction. The studies assisted the developer to use useful software metrics and suitable data mining technique in order to enhance the software quality.…”

Section: Related Workmentioning

confidence: 99%

Software Bug Prediction using Machine Learning Approach

Hammouri¹,

Hammad²,

Alnabhan³

et al. 2018

ijacsa

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Abstract-Software Bug Prediction (SBP) is an important issue in software development and maintenance processes, which concerns with the overall of software successes. This is because predicting the software faults in earlier phase improves the software quality, reliability, efficiency and reduces the software cost. However, developing robust bug prediction model is a challenging task and many techniques have been proposed in the literature. This paper presents a software bug prediction model based on machine learning (ML) algorithms. Three supervised ML algorithms have been used to predict future software faults based on historical data. These classifiers are Naïve Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANNs). The evaluation process showed that ML algorithms can be used effectively with high accuracy rate. Furthermore, a comparison measure is applied to compare the proposed prediction model with other approaches. The collected results showed that the ML approach has a better performance.

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Section: Related Workmentioning

confidence: 99%

Software Bug Prediction using Machine Learning Approach

Hammouri¹,

Hammad²,

Alnabhan³

et al. 2018

ijacsa

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“…In [5], predictive models are estimated based on various code attributes to assess the likelihood of software modules containing errors. Many classification methods have been suggested to accomplish this task.…”

Section: Background Of Workmentioning

confidence: 99%

Data Mining Techniques in Multiple Cancer Prediction

Periasamy¹,

Arutchelvan²

2017

IJARCSSE

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Abstract-Data mining intends to endow with a systematic survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today's medical research. Discussion is made to enable the disease diagnosis and the breakthrough of hidden healthcare patterns from related databases is offered. Also, the use of data mining to discover such relationships as those between health conditions and a disease is presented. It further discusses about the tools that can be used for the processing and classification of data. This paper summarizes various technical articles on medical diagnosis and prognosis. Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. It strives to improve software quality and testing efficiency by constructing predictive models from code attributes to enable a timely identification of faultprone modules. The main objective of paper is to help developers identify defects based on existing software metrics using data mining techniques and thereby improve the software quality. In this paper, we will discuss data mining techniques that are association mining, classification and clustering for lung cancer prediction.

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“…The double bar notation in the activation equation indicates that we are taking the Euclidean distance between x and µ, and squaring the result. For a 1-dimensional Gaussian, this simplifies to just (x -µ) 2 .…”

Section: Rbfnn Implementationmentioning

confidence: 99%

“…Software functional quality reflects functional requirements whereas architectural quality emphasizes non-functional requirements. The objective of software product quality engineering is to achieve the required quality of the product through the definition of quality requirements and their implementation, measurement of appropriate quality attributes and evaluation of the resulting quality [2].…”

Section: Introductionmentioning

confidence: 99%

“…There exists a correlation between the software metrics and the existence of a fault in the software. A software defect prediction model consists of independent variables (software metrics) collected and measured during software development life cycle and a dependent variable (defective or non-defective software) [2]. Wahono [4] provides a systematic literature review of software defect prediction including research trends, datasets, methods and frameworks.…”

Section: Introductionmentioning

confidence: 99%

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Software Defect Prediction Using Radial Basis and Probabilistic Neural Networks

Mehdi¹

2016

IJCATR

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Defects in modules of software systems is a major problem in software development. There are a variety of data mining techniques used to predict software defects such as regression, association rules, clustering, and classification. This paper is concerned with classification based software defect prediction. This paper investigates the effectiveness of using a radial basis function neural network and a probabilistic neural network on prediction accuracy and defect prediction. The conclusions to be drawn from this work is that the neural networks used in here provide an acceptable level of accuracy but a poor defect prediction ability. Probabilistic neural networks perform consistently better with respect to the two performance measures used across all datasets. It may be advisable to use a range of software defect prediction models to complement each other rather than relying on a single technique.

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A Study on Software Metrics based Software Defect Prediction using Data Mining and Machine Learning Techniques

Abstract: Abstract

Cited by 45 publications

References 35 publications

Software Bug Prediction using Machine Learning Approach

Software Bug Prediction using Machine Learning Approach

Data Mining Techniques in Multiple Cancer Prediction

Software Defect Prediction Using Radial Basis and Probabilistic Neural Networks

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