Predicting the complexity of code changes using entropy based measures

Chaturvedi, Krishna Kumar; Kapur, P. K.; Anand, Sameer; Singh, Vir

doi:10.1007/s13198-014-0226-5

Cited by 25 publications

(10 citation statements)

References 13 publications

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“…For all the releases of every product we have taken maximum R 2 across all the four cases defined in Tab. 3. Results show that we have 21 cases of maximum R 2 for case 4 (proposed model), 18 cases of maximum R 2 for case 3 (proposed model), 5 cases of maximum R 2 for case 2 and 4 cases of maximum R 2 for case 1 out of total 48 cases of maximum R 2 across all the products and releases.…”

Section: Numerical Illustrationmentioning

confidence: 88%

“…6 show parameter estimates and performance measures for issues of different products for different cases given in Tab. 3. In shows the real number of issues fixed in n th release of the software product.…”

Section: Numerical Illustrationmentioning

confidence: 99%

“…In order to fix the issues a lot of changes must be made in the source code. These modifications to the source code were quantified using measurement dependent upon entropy called the "complexity of code changes" [2,3]. Code changes are quantified based on Shannon entropy [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Entropy based Software Reliability Growth Modelling for Open Source Software Evolution

et al. 2020

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During Open Source Software (OSS) development, users submit "new features (NFs)", "feature improvements (IMPs)" and bugs to fix. A proportion of these issues get fixed before the next software release. During the introduction of NFs and IMPs, the source code files change. A proportion of these source code changes may result in generation of bugs. We have developed calendar time and entropy-dependent mathematical models to represent the growth of OSS based on the rate at which NFs are added, IMPs are added, and bugs introduction rate.The empirical validation has been conducted on five products, namely "Avro, Pig, Hive, jUDDI and Whirr" of the Apache open source project. We compared the proposed models with eminent reliability growth models, Goel and Okumoto (1979) and Yamada et al. (1983) and found that the proposed models exhibit better goodness of fit.

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Section: Numerical Illustrationmentioning

confidence: 88%

Section: Numerical Illustrationmentioning

confidence: 99%

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Entropy based Software Reliability Growth Modelling for Open Source Software Evolution

et al. 2020

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“…-One problem with decision trees and more generally machine learning techniques is imbalanced data sets for model training [10]. Therefore, the data set used rarely provided even sample sizes of each set without taking necessary precautions the result will be biased by the algorithm.…”

Section: Machine Learningmentioning

confidence: 99%

Automating Software Development using Artificial Intelligence

Nagulapati¹,

Rapelli²,

Fiaidhi³

2020

Preprint

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“…Singh et al [ 52 ] presented a mathematical model using entropy for bug prediction. Chaturvedi et al [ 53 ] proposed a model to predict the bugs based on the current year complexity of code changes/entropy. Key difference between the proposed research work and the existing research papers has been summarized in Table 1 .…”

Section: Related Work and Motivationmentioning

confidence: 99%

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

Gupta¹,

Suri

Kumar³

et al. 2018

Entropy

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Abstract:The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Rényi and Tsallis entropy). By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE)). The values of model performance statistics (R 2 , adjusted R 2 , Mean Square Error (MSE) and standard error) also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers.

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Predicting the complexity of code changes using entropy based measures

Cited by 25 publications

References 13 publications

Entropy based Software Reliability Growth Modelling for Open Source Software Evolution

Entropy based Software Reliability Growth Modelling for Open Source Software Evolution

Automating Software Development using Artificial Intelligence

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

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