New programming languages (e.g., Swift, Go, Rust, etc.) are being introduced to provide a better opportunity for the developers to make software development robust and easy. At the early stage, a ...programming language is likely to have resource constraints that encourage the developers to seek help frequently from experienced peers active in Question–Answering (QA) sites such as Stack Overflow (SO).
In this study, we have formally studied the discussions on three popular new languages introduced after the inception of SO (2008) and match those with the relevant activities in GitHub whenever appropriate. For that purpose, we have mined 4,17,82,536 questions and answers from SO and 7,846 issue information along with 6,60,965 repository information from Github. Initially, the development of new languages is relatively slow compared to mature languages (e.g., C, C++, Java). The expected outcome of this study is to reveal the difficulties and challenges faced by the developers working with these languages so that appropriate measures can be taken to expedite the generation of relevant resources.
We have used the Latent Dirichlet Allocation (LDA) method on SO’s questions and answers to identify different topics of new languages. We have extracted several features of the answer pattern of the new languages from SO (e.g., time to get an accepted answer, time to get an answer, etc.) to study their characteristics. These attributes were used to identify difficult topics. We explored the background of developers who are contributing to these languages. We have created a model by combining Stack Overflow data and issues, repository, user data of Github. Finally, we have used that model to identify factors that affect language evolution.
The major findings of the study are: (i) migration, data and data structure are generally the difficult topics of new languages, (ii) the time when adequate resources are expected to be available vary from language to language, (iii) the unanswered question ratio increases regardless of the age of the language, and (iv) there is a relationship between developers’ activity pattern and the growth of a language.
We believe that the outcome of our study is likely to help the owner/sponsor of these languages to design better features and documentation. It will also help the software developers or students to prepare themselves to work on these languages in an informed way.
Sarcasm detection research in Bengali is still limited due to a lack of relevant resources. In this context, getting high-quality annotated data is costly and time-consuming. Therefore, in this ...paper, we present a transformer-based generative adversarial learning for sarcasm detection from Bengali text based on available limited labeled data. Here, we use the Bengali sarcasm dataset ‘Ben-Sarc’. Besides, we construct another dataset containing Bengali sarcastic and non-sarcastic comments from YouTube and newspapers to observe the model's performance on the new dataset. On top of that, we utilize another Bengali sarcasm dataset ‘BanglaSarc’ to further prove our models' robustness. Among all models, the Bangla BERT-based Generative Adversarial Model has achieved the highest accuracy with 77.1% for the ‘Ben-Sarc’ dataset. Besides, this model has achieved the highest accuracy of 68.2% for the dataset constructed from YouTube and newspaper, and 97.2% for the ‘BanglaSarc’ dataset.
•We propose a method to detect sarcasm from Bengali text by Transformer-based Generative Adversarial Models.•Besides, we correctly classify the confusing text with a limited number of labeled examples by the same model.•Then, we prepare a dataset of Bengali comments from newspapers and YouTube and observe the model's performance over that.•Besides, we utilize another Bengali sarcasm dataset to know the models' robustness.•Finally, a comprehensive comparative study is performed to find out the best results.
Ensuring travelers' safety on roads has become a research challenge in recent years. We introduce a novel safe route planning problem and develop an efficient solution to ensure travelers' safety on ...roads. Though few research attempts have been made in this regard, all of them assume that people share their sensitive travel experiences with a centralized entity for finding the safest routes, which is not ideal in practice for privacy reasons. Furthermore, existing works formulate safe route planning in ways that do not meet a traveler's need for safe travel on roads. Our approach finds the safest routes within a user-specified distance threshold based on the personalized travel experience of the knowledgeable crowd without involving any centralized computation. We develop a privacy-preserving model to quantify the travel experience of a user into personalized safety scores. Our algorithms, direct and iterative for finding the safest route further enhance user privacy by minimizing the exposure of personalized safety scores with others. Our safe route planner can find the safest routes for individuals and groups by considering both a fixed and a set of flexible destination locations. Extensive experiments using real datasets show that our approach finds the safest route in seconds. Compared to the direct algorithm, our iterative algorithm requires 43% less exposure of personalized safety scores.
The blockchain technology has potential applications in various areas such as smart-contracts, Internet of Things (IoT), land registry, supply chain management, storing medical data, and identity ...management. Although GitHub currently hosts more than six thousand active Blockchain software (BCS) projects, few software engineering researchers have investigated these projects and their contributors. Although the number of BCS projects is growing rapidly, the motivations, challenges, and needs of BCS developers remain a puzzle. Therefore, the primary objective of this study is
to understand the motivations, challenges, and needs of BCS developers and analyze the differences between BCS and non-BCS development
. On this goal, we sent an online survey to 1,604 active BCS developers identified by mining the GitHub repositories of 145 popular BCS projects. The survey received 156 responses that met our criteria for analysis. The results suggest that the majority of the BCS developers are experienced in non-BCS development and are primarily motivated by the ideology of creating a decentralized financial system. Although most of the BCS projects are Open Source Software (OSS) projects by nature, more than 93% of our respondents found BCS development somewhat different from a non-BCS development as BCS projects have higher emphasis on security and reliability than most of the non-BCS projects. Other differences include: higher costs of defects, decentralized and hostile environment, technological complexity, and difficulty in upgrading the software after release. These differences were also the primary sources of challenges to them. Software development tools that are tuned for non-BCS development are inadequate for BCS and the ecosystem needs an array of new or improved tools, such as: customized IDE for BCS development tasks, debuggers for smart-contracts, testing support, easily deployable simulators, and BCS domain specific design notations.
Unauthorized access to sensitive or confidential data results in a data breach, which can cause significant harm to an organization. Reporting breaches and reviewing prior records can help reduce ...damages. To aid in preparation, antivirus and security companies have published data breach reports, but they can be difficult to comprehend and require substantial effort to study. This article proposes a data breach incident classification framework using machine learning algorithms (naive Bayes, logistic regression, support vector machine, and random forest) on a dataset from the Privacy Rights Clearinghouse. The framework’s performance is evaluated using various metrics, including accuracy, F1 score, and confusion matrix. The article also employs topic modeling with latent Dirichlet allocation to enhance the classification’s accuracy.
The modern code review process is an integral part of the current software development practice. Considerable effort is given here to inspect code changes, find defects, suggest an improvement, and ...address the suggestions of the reviewers. In a code review process, several iterations usually take place where an author submits code changes and a reviewer gives feedback until is happy to accept the change. In around 12% cases, the changes are abandoned, eventually wasting all the efforts.
In this research, our objective is to design a tool that can predict whether a code change would be merged or abandoned at an early stage to reduce the waste of efforts of all stakeholders (e.g., program author, reviewer, project management, etc.) involved. The real-world demand for such a tool was formally identified by a study by Fan et al. (2018).
We have mined 146,612 code changes from the code reviews of three large and popular open-source software and trained and tested a suite of supervised machine learning classifiers, both shallow and deep learning-based. We consider a total of 25 features in each code change during the training and testing of the models. The features are divided into five dimensions: reviewer, author, project, text, and code.
The best performing model named PredCR (Predicting Code Review), a LightGBM-based classifier achieves around 85% AUC score on average and relatively improves the state-of-the-art (Fan et al., 2018) by 14%–23%. In our extensive empirical study involving PredCR on the 146,612 code changes from the three software projects, we find that (1) The new features like reviewer dimensions that are introduced in PredCR are the most informative. (2) Compared to the baseline, PredCR is more effective towards reducing bias against new developers. (3) PredCR uses historical data in the code review repository and as such the performance of PredCR improves as a software system evolves with new and more data.
PredCR can help save time and effort by helping developers/code reviewers to prioritize the code changes that they are asked to review. Project management can use PredCR to determine how code changes can be assigned to the code reviewers (e.g., select code changes that are more likely to be merged for review before the changes that might be abandoned).
We introduce a novel safe route planning problem and develop an efficient solution to ensure the travelers' safety on roads. Though few research attempts have been made in this regard, all of them ...assume that people share their sensitive travel experiences with a centralized entity for finding the safest routes, which is not ideal in practice for privacy reasons. Furthermore, existing works formulate the safe route planning query in ways that do not meet a traveler's need for safe travel on roads. Our approach finds the safest routes within a user-specified distance threshold based on the personalized travel experience of the knowledgeable crowd without involving any centralized computation. We develop a privacy preserving model to quantify the travel experience of a user into personalized safety scores. Our algorithms for finding the safest route further enhance user privacy by minimizing the exposure of personalized safety scores with others. We implement a working prototype of our solution on the Android platform. Extensive experiments using real datasets show that our approach finds the safest route in seconds with 50% less exposure of personalized safety scores.
Multiple sequence alignment (MSA) is a preliminary task for estimating phylogenies. It is used for homology inference among the sequences of a set of species. Generally, the MSA task is handled as a ...single-objective optimization process. The alignments computed under one criterion may be different from the alignments generated by other criteria, inferring discordant homologies and thus leading to different hypothesized evolutionary histories relating the sequences. The multiobjective (MO) formulation of MSA has recently been advocated by several researchers, to address this issue. An MO approach independently optimizes multiple (often conflicting) objective functions at the same time and outputs a set of competitive alignments. However, no conceptual or experimental rational from a real-world application perspective has been reported so far for any MO formulation of MSA. This article work investigates the impact of MO formulation in the context of an important scientific problem, namely, phylogeny estimation. Employing popular evolutionary MO algorithms, we show that: 1) trees inferred based on alignments produced by the existing MSA methods used in practice are substantially worse in quality than the trees inferred based on the alignment's output by an MO algorithm and 2) even high-quality alignments (according to popular measures available in the literature) may fail to achieve acceptable accuracy in generating phylogenetic trees. Thus, we essentially ask the following natural question: "can a phylogeny-aware (i.e., application-aware) metric guide in selecting appropriate MO formulations to ensure better phylogeny estimation?" Here, we report a carefully designed extensive experimental study that positively answers this question.
StackOverflow (SO), the most popular community Q&A site rewards answerers with reputation scores to encourage answers from volunteer participants. However, irrespective of the difficulty of a ...question, the contributor of an accepted answer is awarded with the same 'reputation' score, which may demotivate an user's additional efforts to answer a difficult question. To facilitate a question difficulty aware rewarding system, this study proposes SOQDE (Stack Overflow Question Difficulty Estimation), a supervised learning based Question difficulty estimation model for the StackOverflow. To design SOQDE, we randomly selected 936 questions from a SO datadump exported during September 2017. Two of the authors independently labeled those questions into three categories (basic, intermediate, or advanced), where conflicting labels were resolved through tie-breaking votes from a third author. We performed an empirical study to determine how the difficulty of a question impacts its outcomes, such as number of votes, resolution time, and number of votes. Our results suggest that the answers of a basic question receive more votes and therefore would generate more reputation points for an answerer. Due to less incentives relative to efforts spent by an answerer, intermediate and advanced questions encounter significantly more delays than the basic questions, which further validates the need of a model like SOQDE. To build our model, we have identified textual and contextual features of a question and divided them into two categories-pre-hoc and post-hoc features. We observed a model based on Random Forest achieving the highest mean accuracy (67.6%), using only answer-independent pre-hoc features. Accommodating answer-dependent post-hoc features, we were able to improve the mean accuracy of our model to 75.2%.
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