In requirements engineering (RE), anaphoric ambiguity is a frequent cause of misunderstandings. It can have a detrimental effect on the quality of requirements and jeopardize the success of a project. If stakeholders of the system, such as testers, developers, or customers, have different understandings or interpretations of software requirements, the system may not be accepted during customer validation. Despite its significance, there has been limited investigation into anaphoric ambiguity in RE. However, focusing on both recognizing and solving uncertainty can be more advantageous than just identifying it. Therefore we investigated the effectiveness of various QA learning techniques including encoder-based and text generation-based NLP models for two goals. We conduct detailed numerical experiments using various transformer models on two public requirements datasets and one generic dataset. Our results indicated that our QA architecture exhibits superior performance compared to baseline models in detecting ambiguity as well as resolving anaphora in contrast to other baseline approaches. We showed that our developed architecture can automatically support requirement development to minimize interpretation risk between stakeholders.

Sentiment analysis of financial news and social media messages along with movement of stock prices could aid in improving the forecasting accuracy of stock prices. In this regard, we aim to perform sentiment analysis of a financial microblog, namely, StockTwits. We carried out the analysis on labelled messages of twelve stocks for a period of five months ranging from May 2019 to September 2019 using various Deep Learning (DL) approaches. We compared the performance of the DL classifiers with traditional machine learning approaches. Long Short Term Memory (LSTM) model and its variations such as bidirectional LSTM and bidrirectional LSTM with dropout outperformed other classifiers. Though use of dropout mechanism did not improve the performance of the model but there was a decrease in bias and variance. Further, we evaluated the performance of various optimizers such as rmsprop, adam, adagrad, adamax and nadam on LSTM. The success rate of all optimizers was similar.

In this study, filtered network approaches such as Minimum Spanning Tree and Planar Maximally Filtered Graphs, are used to analyse the topological structure of constituents of S&P Toronto Stock Exchange Composite Index for a period of three years from January 1, 2015 till Decemeber 31, 2017. For this purpose, rolling correlation for each pair of stocks was calculated for six different time windows of 1, 2, 3, 4, 6 and 12 months. Based on the topological structure, the stocks were categorized into core and peripheral stocks using network measures such as degree centrality, betweenness centrality, eccentricity and eigenvector centrality. Categorization of stocks into core and peripheral was consistent for both MST and PMFG based networks for all time windows. Financial stocks were found to be core stocks. Topological structure helps to understand the inter-relationship among the stocks. It would aid in interpreting the nature of economic factors affecting similar group of stocks. Identification and categorisation of core and peripheral stocks could be used as a base for construction of portfolios and risk management.

Complex dynamics of stock market could be attributed to various factors ranging from company's financial ratios to investors' sentiment and reaction to Financial news. The paper aims to classify Financial news articles as "hot" (significant) and "non-hot" (non-significant). The study is carried out using Dow Jones newswires text feed for a period of four years spanning from 2013 till 2017. Bag-of-ngrams appraoch and Term Frequency-Inverse Document Frequency (TF-IDF) were used for text representation and text weighting, respectively. Four linear classifiers, namely, Logistic Regression (LR), Support Vector Machine (SVM), k Nearest Neighbours (kNN) and multinomial Naïve Bayes (mNB) were used. Grid search was used for hyperparameter optimisation. Performance of the classifiers was evaluated using five measures, namely, success rate, precision, recall, F1 measure and area under receiver operating characteristics curve. LR and SVM outperformed other models in terms of all five performance measures for both Bag-of-ngrams model and Bag-of-ngrams model with TF-IDF approach. Use of TF-IDF improved performance of the classifiers, especially, in case of mNB. This study serves as a stepping stone in identification of important/relevant news, which could used as predictors for stock price forecasting.

The same defect can be rediscovered by multiple clients, causing unplanned outages and leading to reduced customer satisfaction. In the case of popular open source software, high volume of defects is reported on a regular basis. A large number of these reports are actually duplicates / rediscoveries of each other. Researchers have analyzed the factors related to the content of duplicate defect reports in the past. However, some of the other potentially important factors, such as the inter-relationships among duplicate defect reports, are not readily available in defect tracking systems such as Bugzilla. This information may speed up bug fixing, enable efficient triaging, improve customer profiles, etc. In this paper, we present three defect rediscovery datasets mined from Bugzilla. The datasets capture data for three groups of open source software projects: Apache, Eclipse, and KDE. The datasets contain information about approximately 914 thousands of defect reports over a period of 18 years (1999-2017) to capture the inter-relationships among duplicate defects. We believe that sharing these data with the community will help researchers and practitioners to better understand the nature of defect rediscovery and enhance the analysis of defect reports.

The Twitter Bullishness Index (TBI) has previously been reported to be a social media analytics indicator for the stock market. We explore the different components that shape the TBI. First, we determine the users to be dominated by the US and the English-speaking world. Second, we applied the Natural Language Processing tool term frequency--inverse document frequency (TFIDF) to explore the tweet content that contribute to the TBI. We revealed that the keyword lexicon associated with the terms bullish and bearish differs. Finally, we used the vector autoregression framework. Our results do not show the TBI to be a leading indicator for the returns of the Dow Jones Industrial Average (DJIA) within statistical significance. However, a Pearson correlation of 0.49 is observed between the TBI and the DJIA return.

The continuous growth of e-commerce has stimulated great interest in generating theories and models for online consumer behavior. While studies on online consumer behavior are widespread, research on relating Internet browsing activities to online shopping behavior are scarce. This paper provides an exploratory analysis on the relationship between online browsing habits and consumers' pre-shopping effort, as one of the indicators of shopping behavior. The data used in this study was extracted from 88,637 users with more than half a million shopping instances from two large online retailers, Amazon and Walmart. Our findings provide insights for scholars to form hypotheses and design models or theories to explain online consumer behavior. Practitioners may also use the results of this study to make strategic decisions.

In software engineering, maintainability is related to investigating the defects and their causes, correcting the defects and modifying the system to meet customer requirements. Maintenance is a time consuming activity within the software life cycle. Therefore, there is a need for efficiently organizing the software resources in terms of time, cost and personnel for maintenance activity. One way of efficiently managing maintenance resources is to predict defects that may occur after the deployment. Many researchers so far have built defect prediction models using different sets of metrics such as churn and static code metrics. However, hidden causes of defects such as code smells have not been investigated thoroughly. In this study we propose using data science and analytics techniques on software data to build defect prediction models. In order to build the prediction model we used code smells metrics, churn metrics and combination of churn and code smells metrics. The results of our experiments on two different software companies show that code smells is a good indicator of defect proneness of the software product. Therefore, we recommend that code smells metrics should be used to train a defect prediction model to guide the software maintenance team.

The bug tracking repositories of software projects capture initial defect (bug) reports and the history of interactions among developers, testers, and customers. Extracting and mining information from these repositories is time consuming and daunting. Researchers have focused mostly on analyzing the frequency of the occurrence of defects and their attributes (e.g., The number of comments and lines of code changed, count of developers). However, the counting process eliminates information about the temporal alignment of events leading to changes in the attributes count. Software quality teams could plan and prioritize their work more efficiently if they were aware of these temporal sequences and knew their frequency of occurrence. In this paper, we introduce a novel dataset mined from the Fire fox bug repository (Bugzilla) which contains information about the temporal alignment of developer interactions. Our dataset covers eight years of data from the Fire fox project on activities throughout the project's lifecycle. Some of these activities have not been reported in frequency-based or other temporal datasets. The dataset we mined from the Fire fox project contains new activities, such as reporter experience, file exchange events, code-review process activities, and setting of milestones. We believe that this new dataset will improve analysis of bug reports and enable mining of temporal relationships so that practitioners can enhance their bug-fixing process.

Web service discovery is currently performed with centralized registries such as UDDI. In this paper, we propose a super-peer network protocol to combine the efficiency of a centralized protocols and P2P networks. For avoiding a flooding the network with search request and for minimizing the number of messages routed in the network, we represented content-addressable network (CAN) structure, which provides a scalable, fault-tolerant distributed hash table (DHT), for super-peers communication. Web service definitions implemented semantically as OWL ontology. The proposed architecture offers self-maintaining and self-clustering network where the peer groups classify the Web service definitions and each peer-group becomes the owner of a classification dynamically.