@article{mahdavi-hezaveh_dremann_williams_2021, title={Software development with feature toggles: practices used by practitioners}, volume={26}, ISSN={["1573-7616"]}, DOI={10.1007/s10664-020-09901-z}, abstractNote={Background: Using feature toggles is a technique that allows developers to either turn a feature on or off with a variable in a conditional statement. Feature toggles are increasingly used by software companies to facilitate continuous integration and continuous delivery. However, using feature toggles inappropriately may cause problems which can have a severe impact, such as code complexity, dead code, and system failure. For example, the erroneous repurposing of an old feature toggle caused Knight Capital Group, an American global financial services firm, to go bankrupt due to the implications of the resultant incorrect system behavior. Aim: The goal of this research project is to aid software practitioners in the use of practices to support software development with feature toggles through an empirical study of feature toggle practice usage by practitioners. Method: We conducted a qualitative analysis of 99 artifacts from the grey literature and 10 peer-reviewed papers about feature toggles. We conducted a survey of practitioners from 38 companies. Results: We identified 17 practices in 4 categories: Management practices, Initialization practices, Implementation practices, and Clean-up practices. We observed that all of the survey respondents use a dedicated tool to create and manage feature toggles in their code. Documenting feature toggle's metadata, setting up the default value for feature toggles, and logging the changes made on feature toggles are also frequently-observed practices. Conclusions: The feature toggle development practices discovered and enumerated in this work can help practitioners more effectively use feature toggles. This work can enable future mining of code repositories to automatically identify feature toggle practices.}, number={1}, journal={EMPIRICAL SOFTWARE ENGINEERING}, author={Mahdavi-Hezaveh, Rezvan and Dremann, Jacob and Williams, Laurie}, year={2021}, month={Jan} }
 @article{rahman_mahdavi-hezaveh_williams_2020, title={A Literature Review on Mining Cyberthreat Intelligence from Unstructured Texts}, ISSN={["2375-9232"]}, DOI={10.1109/ICDMW51313.2020.00075}, abstractNote={Cyberthreat defense mechanisms have become more proactive these days, and thus leading to the increasing incorporation of cyberthreat intelligence (CTI). Cybersecurity researchers and vendors are powering the CTI with large volumes of unstructured textual data containing information on threat events, threat techniques, and tactics. Hence, extracting cyberthreat-relevant information through text mining is an effective way to obtain actionable CTI to thwart cyberattacks. The goal of this research is to aid cybersecurity researchers understand the source, purpose, and approaches for mining cyberthreat intelligence from unstructured text through a literature review of peer-reviewed studies on this topic. We perform a literature review to identify and analyze existing research on mining CTI. By using search queries in the bibliographic databases, 28,484 articles are found. From those, 38 studies are identified through the filtering criteria which include removing duplicates, non-English, non-peer-reviewed articles, and articles not about mining CTI. We find that the most prominent sources of unstructured threat data are the threat reports, Twitter feeds, and posts from hackers and security experts. We also observe that security researchers mined CTI from unstructured sources to extract Indicator of Compromise (IoC), threat-related topic, and event detection. Finally, natural language processing (NLP) based approaches: topic classification; keyword identification; and semantic relationship extraction among the keywords are mostly availed in the selected studies to mine CTI information from unstructured threat sources.}, journal={20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2020)}, author={Rahman, Md Rayhanur and Mahdavi-Hezaveh, Rezvan and Williams, Laurie}, year={2020}, pages={516–525} }