Software maintenance is an essential phase of software development. Developers employ issue tracking systems to collect bugs for software improvement. Users submit bugs through such issue tracking systems and decide the severity of reported bugs. The severity is an important attribute of a bug that decides how quickly it should be solved. It helps developers to solve important bugs on time. However, manual severity assessment is a tedious job and could be incorrect. To this end, in this paper, we propose a deep neural network-based automatic approach for the severity prediction of bug reports. First, we apply natural language processing techniques for text preprocessing of bug reports. Second, we compute and assign an emotion score for each bug report. Third, we create a vector for each preprocessed bug report. Forth, we pass the constructed vector and the emotion score of each bug report to a deep neural network based classifier for severity prediction. We also evaluate the proposed approach on the history-data of bug reports. The results of cross-product suggest that the proposed approach outperforms the state-of-the-art approaches. On average, it improves the f-measure by 7.90% .

An app store (i.e., Google Play) is a platform for mobile apps for almost every software and service. App stores allow users to browse and download apps and facilitate developers to keep an eye on their apps by providing ratings and reviews of the apps. App reviews may include the user’s experience, information about bugs, request for new features, or rating of the app in word. The manual categorization of app reviews is critical and time-consuming for developers. Automatic classification of app reviews may help developers especially for fixing bugs on time. In this perspective, several approaches have been proposed for the automatic classification of reviews. However, none of them exploits the non-textual information of app reviews. In this paper, we propose a deep learning based approach for the classification of app reviews. It does not only leverage non-textual information of app reviews but also exploits a deep learning technique that has proved more accurate for the text classification in various domains. The approach first extracts textual and nontextual information of each app review, preprocesses the textual information, computes the sentiment of app reviews using Senti4SD, and determines the history of the reviewer includes the total number of reviews posted by the reviewer, and his submission rate (i.e., what percentages of his review have been submitted for the associated app). Second, we create a digital vector against each app review. Finally, we train a deep learning based multi-class classifier to classify app reviews. The proposed approach is evaluated on a public dataset, and the results suggest that it significantly improves the state of the art. It improves average precision from 75.72% to 95.49%, average recall from 69.40% to 93.94%, and f-measure from 72.41% to 94.71%, respectively.

Sentiment analysis holds significant importance in research projects by providing valuable insights into public opinions. However, the majority of sentiment analysis studies focus on the English language, leaving a gap in research for other low-resourced languages or regional languages, e.g., Persian, Pashto, and Urdu. Moreover, computational linguists face the challenge of developing lexical resources for these languages. In light of this, this paper presents a deep learning-based approach for Urdu Text Sentiment Analysis (USA-BERT), leveraging Bidirectional Encoder Representations from Transformers and introduces an Urdu Dataset for Sentiment Analysis-23 (UDSA-23). USA-BERT first preprocesses the Urdu reviews by exploiting BERT-Tokenizer . Second, it creates BERT embeddings for each Urdu review. Third, given the BERT embeddings, it fine-tunes a deep learning classifier (BERT). Finally, it employs the Pareto principle on two datasets (the state-of-the-art (UCSA-21) and UDSA-23) to assess USA-BERT. The assessment results demonstrate that USA-BERT significantly surpasses the existing methods by improving the accuracy and f-measure up to 26.09% and 25.87%, respectively.

Sentiment analysis extracts valuable insights from textual sources using computation, textual or systematic analysis, and natural language processing. It identifies and measures the attitudes, beliefs, and emotional states individuals express through text data. Recent research on sentiment analysis has largely focused on the English language; therefore, low-resource languages are getting much less attention. Conducting sentiment analysis of low-resource languages is difficult because large datasets and related repositories are unavailable. This paper creates a new dataset for low-resource language (Urdu) to address this issue. The dataset, namely LUCSA-23, consists of more than 65,000 user reviews from various genres, including food, sports, showbiz, apps, and political reviews from developing countries, i.e., Pakistan. Urdu domain experts further annotate the created dataset. This paper proposes an Urdu sentiment analysis approach leveraging the transformer model, i.e., XLM-R and GPT-2. It preprocesses the Urdu text input, generates BERT embeddings, and passes them to the proposed classifier as input for sentiment classification. The proposed classifier is compared with machine/deep/embedded classifiers to evaluate its performance. The findings show that the proposed classifiers outperform existing state-of-the-art approaches with an accuracy of 95%.

In the context of natural-language processing, keyword extraction has been studied widely. In promoting business-enterprise goods and services, however, a major challenge remains to extracting keywords effectively and efficiently from social-media us