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The inclusion of grey literature (GL) is important to remove publication bias while gathering available evidence regarding a certain topic. The number of systematic literature reviews (SLRs) in Software Engineering (SE) is increasing but we do not know about the extent of GL usage in these SLRs. Moreover, Google Scholar is rapidly becoming a search engine of choice for many researchers but the extent to which it can find the primary studies is not known. Objective: This tertiary study is an attempt to i) measure the usage of GL in SLRs in SE. Furthermore this study proposes strategies for categorizing GL and a quality checklist to use for GL in future SLRs; ii) explore if it is feasible to use only Google Scholar for finding scholarly articles for academic research. Method: We have conducted a systematic mapping study to measure the extent of GL usage in SE SLRs as well as to measure the feasibility of finding primary studies using Google Scholar. Results and conclusions: a) Grey Literature: 76.09% SLRs (105 out of 138) in SE have included one or more GL studies as primary studies. Among total primary studies across all SLRs (6307), 582 are classified as GL, making the frequency of GL citing as 9.23%. The intensity of GL use indicate that each SLR contains 5 primary studies on average (total intensity of GL use being 5.54). The ranking of GL tells us that conference papers are the most used form 43.3% followed by technical reports 28.52%. Universities, research institutes, labs and scientific societies together make up 67.7% of GL used, indicating that these are useful sources for searching GL. We additionally propose strategies for categorizing GL and criteria for evaluating GL quality, which can become a basis for more detailed guidelines for including GL in future SLRs. b) Google Scholar Results: The results show that Google Scholar was able to retrieve 96% of primary studies of these SLRs. Most of the primary studies that were not found using Google Scholar were from grey sources.

The previous year has seen an enormous increase in the studies related to social engineering. This increase is partly due to increasing number of social engineering attacks and partly due to people's inability to identify the attack. Thus, it is of great importance to find solutions which are helpful for human to understand the social engineering attacks and scenarios. To address this, we have performed a literature review of studies (on social engineering) in top-notch journals and conferences. In this paper, we have enlisted the types of attacks, and the persuasion techniques used by social engineers as listed in the literature. We also combined different theories which researchers tried to use to explain various activities of social engineers. Furthermore, we have mentioned that a better understanding of the social engineering attack scenarios can be done using thematic and game-based analysis techniques.Preliminary empirical evaluation of the proposed game based method shows overall neutral results. Future extension and evaluation is needed for the proposed methods. K E Y W O R D Scybercrime, cyber-security, human factors, human-centered, information security, review, social engineering

Protecting people from cyber threats imposes great challenges, not only technically, but also socially. To achieve the intended level of awareness, software security principles need to be shown with concrete examples during security education. This study aims to design a serious game integrating software security knowledge and concepts into the processes to make it more engaging to learn while playing. In this paper, we have: (i) designed a serious game to compensate the deficiencies in the literature; (ii) performed empirical evaluations including survey, brainstorming and observation to the proposed game. Results: Our study shows that: (i) Cyber Security-Requirements Awareness Game (CSRAG) has a positive effect on players security learning outcomes, level of engagement and participation; (ii) Game-based learning can be an effective way of teaching security related scenarios.

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User forums enable a large population of crowd-users to publicly share their experience, useful thoughts, and concerns about the software applications in the form of user reviews. Recent research studies have revealed that end-user reviews contain rich and pivotal sources of information for the software vendors and developers that can help undertake software evolution and maintenance tasks. However, such user-generated information is often fragmented, with multiple viewpoints from various stakeholders involved in the ongoing discussions in the Reddit forum. In this article, we proposed a crowd-based requirements engineering by valuation argumentation (CrowdRE-VArg) approach that analyzes the end-users discussion in the Reddit forum and identifies conflict-free new features, design alternatives, or issues, and reach a rationale-based requirements decision by gradually valuating the relative strength of their supporting and attacking arguments. The proposed approach helps to negotiate the conflict over the new features or issues between the different crowd-users on the run by finding a settlement that satisfies the involved crowd-users in the ongoing discussion in the Reddit forum using argumentation theory. For this purpose, we adopted the bipolar gradual valuation argumentation framework, extended from the abstract argumentation framework and abstract valuation framework. The automated CrowdRE-VArg approach is illustrated through a sample crowd-users conversation topic adopted from the Reddit forum about Google Map mobile application. Finally, we applied natural language processing and different machine learning algorithms to support the automated execution of the CrowdRE-VArg approach. The results demonstrate that the proposed CrowdRE-VArg approach works as a proof-of-concept and automatically identifies prioritized requirements-related information for software engineers.