Supervised Learning For Orphan Adoption Problem In Software Architecture Recovery

Bibi, Maryum; Maqbool, Onaiza; Kanwal, Jaweria

doi:10.22452/mjcs.vol29no4.4

Cited by 7 publications

(3 citation statements)

References 42 publications

(78 reference statements)

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“…However, one important aspect of the present results is that they do not shed much light on how well CAARIA would scale when evaluated on relatively larger datasets. This could be an important aspect to explore further perhaps by applying our algorithm in other domains like text classification [69], spam detection [70], and architecture recovery [71].…”

Section: Resultsmentioning

confidence: 99%

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Class Association and Attribute Relevancy Based Imputation Algorithm to Reduce Twitter Data for Optimal Sentiment Analysis

et al. 2019

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Twitter sentiment analysis is a challenging task that involves various preprocessing steps including dimensionality reduction. Dimensionality reduction helps ensure low computational complexity and performance improvement during the classification process. In Twitter data, each tweet has feature values which may or may not reflect a person's response. Therefore, a large number of sparse data points are generated when tweets are represented as feature matrix, eventually increasing computational overheads and error rates in Twitter sentiment analysis. This study proposes a novel preprocessing technique called class association and attribute relevancy based imputation algorithm (CAARIA) to reduce the Twitter data size. CAARIA achieves the dimensionality reduction goal by imputing those tweets that belong to the same class and also share useful information. The performance of two classifiers (Naïve Bayes and support vector machines) is evaluated on three Twitter datasets in terms of classification accuracy, measured as area under curve, and time efficiency. CAARIA is also compared against two widely used feature selection (dimensionality reduction) techniques, information gain (IG) and Pearson's correlation (PC). The findings reveal that CAARIA outperforms IG and PC in terms of classification accuracy and time efficiency. These results suggest that CAARIA is a robust data preprocessing technique for the classification task.

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Section: Resultsmentioning

confidence: 99%

“…Other classification techniques including deep recurrent neural network will be investigated for further evaluation. The proposed algorithm will be experimented on relatively larger Twitter datasets and perhaps to other domains including text classification [69], spam detection [70], and architecture recovery [71].…”

Section: Future Workmentioning

confidence: 99%

Class Association and Attribute Relevancy Based Imputation Algorithm to Reduce Twitter Data for Optimal Sentiment Analysis

et al. 2019

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“…Bibi et al evaluated three supervised learning techniques for automatic mapping [4]. They evaluate Naive Bayes, k-Nearest Neighbors, and Neural Nets as classifiers of orphans based on structural criteria from Tzerpos and Holt [29] and compare these with the original structural approach.…”

Section: Related Workmentioning

confidence: 99%

To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes

Olsson¹,

Ericsson²,

Wingkvist³

2021

Preprint

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Background:The process of mapping a source code entity onto an architectural module is to a large degree a manual task. Automating this process could increase the use of static architecture conformance checking methods, such as reflexion modeling, in industry. Current techniques rely on user parameterization and a highly cohesive design. A machine learning approach would potentially require less parameters and better use of the available information to aid in automatic mapping. Aim:We investigate how a classifier can be trained to map from source code to architecture modules automatically. This classifier is trained with semantic and syntactic dependency information extracted from the source code and from architecture descriptions. The classifier is implemented using multinomial naive Bayes and evaluated. Method: We perform experiments and compare the classifier with three state-of-the-art mapping functions in eight open-source Java systems with known ground-truthmappings. Results: We find that the classifier outperforms the state-of-the-art in all cases and that it provides a useful baseline for further research in the area of semi-automatic incremental clustering. Conclusions: We conclude that machine learning is a useful approach that performs better and with less need for parameterization compared to other approaches. Future work includes investigating problematic mappings and a more diverse set of subject systems.

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Optimized Machine Learning Input for Evolutionary Source Code to Architecture Mapping

Olsson

Ericsson

Wingkvist

2023

Lecture Notes in Computer Science

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Supervised Learning For Orphan Adoption Problem In Software Architecture Recovery

Cited by 7 publications

References 42 publications

Class Association and Attribute Relevancy Based Imputation Algorithm to Reduce Twitter Data for Optimal Sentiment Analysis

Class Association and Attribute Relevancy Based Imputation Algorithm to Reduce Twitter Data for Optimal Sentiment Analysis

To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes

Optimized Machine Learning Input for Evolutionary Source Code to Architecture Mapping

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