Towards understanding the role of content-based and contextualized features in detecting abuse on Twitter.

This paper presents a novel approach for detecting abuse on Twitter. Abusive posts have become a major problem for social media platforms like Twitter. It is important to identify abuse to mitigate its potential harm. Many researchers have proposed methods to detect abuse on Twitter. However, most of the existing approaches for detecting abuse look only at the content of the abusive tweet in isolation and do not consider its contextual information, particularly the tweets posted before the abusive tweet. In this paper, we propose a new method for detecting abuse that uses contextual information from the tweets that precede and follow the abusive tweet. We hypothesize that this contextual information can be used to better understand the intent of the abusive tweet and to identify abuse that content-based methods would otherwise miss. We performed extensive experiments to identify the best combination of features and machine learning algorithms to detect abuse on Twitter. We test eight different machine learning classifiers on content- and context-based features for the experiments. The proposed method is compared with existing abuse detection methods and achieves an absolute improvement of around 7%. The best results are obtained by combining the content and context-based features. The highest accuracy of the proposed method is 86%, whereas the existing methods used for comparison have highest accuracy of 79.2%.

Extraction of use case diagram elements using natural language processing and network science.

Software engineering artifact extraction from natural language requirements without human intervention is a challenging task. Out of these artifacts, the use case plays a prominent role in software design and development. In the literature, most of the approaches are either semi-automated or necessitate formalism or make use of restricted natural language for the extraction of use cases from textual requirements. In this paper, we resolve the challenge of automated artifact extraction from natural language requirements. We propose an automated approach to generate use cases, actors, and their relationships from natural language requirements. Our proposed approach involves no human intervention or formalism. To automate the proposed approach, we have used Natural Language Processing and Network Science. Our proposed approach provides promising results for the extraction of use case elements from natural language requirements. We validate the proposed approach using several literature-based case studies. The proposed approach significantly improves the results in comparison to an existing approach. On average, the proposed approach achieves around 71.5% accuracy (F-Measure), whereas the baseline method achieves around 16% accuracy (F-Measure) on average. The evaluation of the proposed approach on the literature-based case studies shows its significance for the extraction of use case elements from natural language requirements. The approach reduces human effort in software design and development.

IDLIQ: An Incremental Deterministic Finite Automaton Learning Algorithm Through Inverse Queries for Regular Grammar Inference.

We present an efficient incremental learning algorithm for Deterministic Finite Automaton (DFA) with the help of inverse query (IQ) and membership query (MQ). This algorithm is an extension of the Identification of Regular Languages (ID) algorithm from a complete to an incremental learning setup. The learning algorithm learns by making use of a set of labeled examples and by posing queries to a knowledgeable teacher, which is equipped to answer IQs along with MQs and equivalence query. Based on the examples (elements of the live complete set) and responses against IQs from the minimally adequate teacher (MAT), the learning algorithm constructs the hypothesis automaton, consistent with all observed examples. The Incremental DFA Learning algorithm through Inverse Queries (IDLIQ) takes O(|Σ|N+|Pc||F|) time complexity in the presence of a MAT and ensures convergence to a minimal representation of the target DFA with finite number of labeled examples. Existing incremental learning algorithms; the Incremental ID, the Incremental Distinguishing Strings have polynomial (cubic) time complexity in the presence of a MAT. Therefore, sometimes, these algorithms even fail to learn large complex software systems. In this research work, we have reduced the complexity (from cubic to square form) of the DFA learning in an incremental setup. Finally, we prove the correctness and termination of the IDLIQ algorithm.

Historical perspective of code clone refactorings in evolving software.

Cloning in software is generally perceived as a threat to its maintenance and that is why it needs to be managed properly. Understanding clones from a historical perspective is essential for effective clone management. Analysis of code refactorings performed on clones in previous releases will help developers in taking decisions about clone refactoring in future releases. In this paper we perform a longitudinal study on the evolution of clone refactorings in various versions of five software systems. To perform a systematic study on clone refactoring evolution, we define clone evolution patterns for studying refactorings in a formal notation. Our results show that only a small proportion of code clones are refactored between the versions and most of the refactorings are inconsistent within clone classes. Moreover, clone refactorings may cause clone removal. Analysis of the source code of refactored clones reveals similar reasons of inconsistent refactorings and clone removal for five Java systems. This analysis will help in devising appropriate strategies for managing clone refactorings in software and hence provide foundation for devising better clone management tools.

Vovel metrics-novel coupling metrics for improved software fault prediction.

Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability.

Multi-level aspect based sentiment classification of Twitter data: using hybrid approach in deep learning.

Social media is a vital source to produce textual data, further utilized in various research fields. It has been considered an essential foundation for organizations to get valuable data to assess the users' thoughts and opinions on a specific topic. Text classification is a procedure to assign tags to predefined classes automatically based on their contents. The aspect-based sentiment analysis to classify the text is challenging. Every work related to sentiment analysis approached this issue as the current research usually discusses the document-level and overall sentence-level analysis rather than the particularities of the sentiments. This research aims to use Twitter data to perform a finer-grained sentiment analysis at aspect-level by considering explicit and implicit aspects. This study proposes a new Multi-level Hybrid Aspect-Based Sentiment Classification (MuLeHyABSC) approach by embedding a feature ranking process with an amendment of feature selection method for Twitter and sentiment classification comprising of Artificial Neural Network; Multi-Layer Perceptron (MLP) is used to attain improved results. In this study, different machine learning classification methods were also implemented, including Random Forest (RF), Support Vector Classifier (SVC), and seven more classifiers to compare with the proposed classification method. The implementation of the proposed hybrid method has shown better performance and the efficiency of the proposed system was validated on multiple Twitter datasets to manifest different domains. We achieved better results for all Twitter datasets used for the validation purpose of the proposed method with an accuracy of 78.99%, 84.09%, 80.38%, 82.37%, and 84.72%, respectively, compared to the baseline approaches. The proposed approach revealed that the new hybrid aspect-based text classification functionality is enhanced, and it outperformed the existing baseline methods for sentiment classification.

Formalizing the use case model: A model-based approach.

In general, requirements expressed in natural language are the first step in the software development process and are documented in the form of use cases. These requirements can be specified formally using some precise mathematical notation (e.g. Linear Temporal Logic (LTL), Computational Tree Logic (CTL) etc.) or using some modeling formalism (e.g. a Kripke structure). The rigor involved in writing formal requirements requires extra time and effort, which is not feasible in several software development scenarios. A number of existing approaches are able to transform informal software requirements to formal specifications. However, most of these approaches require additional skills like understanding of specification languages additional artifacts, or services of domain expert(s). Consequently, an automated approach is required to reduce the overhead of effort for converting informal requirements to formal specifications. This work introduces an approach that takes a use case model as input in the proposed template and produces a Kripke structure and LTL specifications as output. The proposed approach also considers the common use case relationships (i.e., include and extend). The generated Kripke structure model of the software allows analysis of software behavior early at the requirements specification stage which otherwise would not be possible before the design stage of the software development process. The generated LTL formal specifications can be used against a formal model like a Kripke structure generated during the software development process for verification purpose. We demonstrate the working of the proposed approach by a SIM vending machine example, where the use cases of this system are inputs in the proposed template and the corresponding Kripke structure and LTL formal specifications are produced as final output. Additionally, we use the NuSMV tool to verify the generated LTL specifications against the Kripke structure model of the software, which reports no counterexamples thus validating the proposed approach.