Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug

Kumari, Madhu; Misra, Ananya; Misra, Sanjay; Sanz, Luís Fernández; Damaševičius, Robertas; Singh, Vir

doi:10.3390/e21010091

Cited by 28 publications

(13 citation statements)

References 51 publications

(92 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Entropy based measure has been used to predict the bugs lying dormant in the software [ 14 , 15 ]. Recently entropy based measures have been used to handle the uncertainty during the prediction of priority and severity of the reported bug [ 3 , 13 ].…”

Section: Related Workmentioning

confidence: 99%

Entropy Based Machine Learning Models for Software Bug Severity Assessment in Cross Project Context

Kumari

Singh

Sharma

2020

Computational Science and Its Applications – ICCSA 2020

Self Cite

View full text Add to dashboard Cite

There can be noise and uncertainty in the bug reports data as the bugs are reported by a heterogeneous group of users working across different countries. Bug description is an essential attribute that helps to predict other bug attributes, such as severity, priority, and time fixes. We need to consider the noise and confusion present in the text of the bug report, as it can impact the output of different machine learning techniques. Shannon entropy has been used in this paper to calculate summary uncertainty about the bug. Bug severity attribute tells about the type of impact the bug has on the functionality of the software. Correct bug severity estimation allows scheduling and repair bugs and hence help in resource and effort utilization. To predict the severity of the bug we need software project historical data to train the classifier. These training data are not always available in particular for new software projects. The solution which is called cross project prediction is to use the training data from other projects. Using bug priority, summary weight and summary entropy, we have proposed cross project bug severity assessment models. Results for proposed summary entropy based approach for bug severity prediction in cross project context show improved performance of the Accuracy and F-measure up to 70.23% and 93.72% respectively across all the machine learning techniques over existing work.

show abstract

Section: Related Workmentioning

confidence: 99%

Entropy Based Machine Learning Models for Software Bug Severity Assessment in Cross Project Context

Kumari

Singh

Sharma

2020

Computational Science and Its Applications – ICCSA 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The concept of entropy is one that has been adopted for software measurements on different occasions [2,3,14]. The entropy of a CSS file with n unique classes of elements can be computed using their frequency of occurrence as an unbiased estimate of their probability P(C i ), i = 1, .…”

Section: Entropy Metric (E)mentioning

confidence: 99%

“…One of the approaches to decrease the maintainability effort is by managing the complexity of applications. Another approach is to predict code bug [2] or smells [3] thus contributing to prolonging the lifecycle of programs too. Software complexity is measured in order to, among other things, be able to predict the maintainability and reliability of software given that code metrics can serve as indicators of software defects [4].…”

Section: Introductionmentioning

confidence: 99%

A Complexity Metrics Suite for Cascading Style Sheets

2019

View full text Add to dashboard Cite

We perform a theoretical and empirical analysis of a set of Cascading Style Sheets (CSS) document complexity metrics. The metrics are validated using a practical framework that demonstrates their viability. The theoretical analysis is performed using the Weyuker’s properties−a widely adopted approach to conducting empirical validations of metrics proposals. The empirical analysis is conducted using visual and statistical analysis of distribution of metric values, Cliff’s delta, Chi-square and Liliefors statistical normality tests, and correlation analysis on our own dataset of CSS documents. The results show that five out of the nine metrics (56%) satisfy Weyuker’s properties except for the Number of Attributes Defined per Rule Block (NADRB) metric, which satisfies six out of nine (67%) properties. In addition, the results from the statistical analysis show good statistical distribution characteristics (only the Number of Extended Rule Blocks (NERB) metric exceeds the rule-of-thumb threshold value of the Cliff’s delta). The correlation between the metric values and the size of the CSS documents is insignificant, suggesting that the presented metrics are indeed complexity rather than size metrics. The practical application of the presented CSS complexity metric suite is to assess the risk of CSS documents. The proposed CSS complexity metrics suite allows identification of CSS files that require immediate attention of software maintenance personnel.

show abstract

“…Peng et al proposed to use a search technique exploiting inverted indexed terms from different topics for the bug triage problem [6]. Recently, Kumari et al proposed to use a bug dependency-based mathematical model by interpreting a bug's summary and description in terms of entropy to develop software reliability growth models [7].…”

Section: Introductionmentioning

confidence: 99%

“…http://bugtriage.mybluemix.net/ 6 https://gcc.gnu.org/bugzilla/query.cgi7 https://bugs.eclipse.org/bugs/ 8 https://bz.apache.org/netbeans/query.cgi9 https://bugzilla.mozilla.org/query.cgi?10 https://github.com/farhan-93/bugtriage…”

mentioning

confidence: 99%

Applying Convolutional Neural Networks With Different Word Representation Techniques to Recommend Bug Fixers

et al. 2020

View full text Add to dashboard Cite

Bug triage processes are intended to assign bug reports to appropriate developers effectively, but they typically become bottlenecks in the development process-especially for large-scale software projects. Recently, several machine learning approaches, including deep learning-based approaches, have been proposed to recommend an appropriate developer automatically by learning past assignment patterns. In this paper, we propose a deep learning-based bug triage technique using a convolutional neural network (CNN) with three different word representation techniques: Word to Vector (Word2Vec), Global Vector (GloVe), and Embeddings from Language Models (ELMo). Experiments were performed on datasets from well-known large-scale open-source projects, such as Eclipse and Mozilla, and top-k accuracy was measured as an evaluation metric. The experimental results suggest that the ELMo-based CNN approach performs best for the bug triage problem. GloVe-based CNN slightly outperforms Word2Vec-based CNN in many cases. Word2Vec-based CNN outperforms GloVe-based CNN when the number of samples per class in the dataset is high enough.

show abstract

Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug

Cited by 28 publications

References 51 publications

Entropy Based Machine Learning Models for Software Bug Severity Assessment in Cross Project Context

Entropy Based Machine Learning Models for Software Bug Severity Assessment in Cross Project Context

A Complexity Metrics Suite for Cascading Style Sheets

Applying Convolutional Neural Networks With Different Word Representation Techniques to Recommend Bug Fixers

Contact Info

Product

Resources

About