Search

In interactive narratives, the progress of the story is not pre-determined by the author. In other words, the player takes an active role in deciding how the story should unfold. To design an immersive interactive narrative, authors have to deal with an inevitable degree of complexity. Therefore, developing authoring tools to help authors create more complex interactive narratives is valuable. In this regard, different authoring systems have been proposed in the past. However, only a few of them are designed atop a rigorous formal foundation. One of the less explored potentials of such a foundation is improving narrative validation capabilities. In this paper, we introduce a novel framework for authoring interactive narratives. This framework provides a formal foundation for modelling and validating interactive narratives. We report on two case studies to show the capacity of the proposed framework. Also, we conducted a user study to examine the proposed framework with regard to narrative debugging and validation. Case studies demonstrated the capacity of the proposed framework for modelling different types of narratives. Moreover, the user study showed that the proposed framework facilitates assertion checking and debugging. [ABSTRACT FROM AUTHOR]

Problem-solving is placed at top of the ten most important life skills recommended by World Economic Forum. Serious game has been one of the dominant tools for educating people in different subjects over the last 10 years. This study aims to critically analyse previous empirical studies conducted on Problem-Solving Serious Games (PSSG). Using Constant Comparative Method (CCM) framework, 737 studies in this area are listed; and after applying several filters, 36 studies are refined and finally chosen. The analysis shows that serious games not alone improve people's problem-solving ability, it also positively impacts on their problem-solving motivation, problem-solving measurement ability, critical thinking, and learning attitude. However, there is a lack of pedagogists' engagement with PSSG studies. As a result, those studies suffered from lack of educational rationale behind and lack of right direction in choosing their participants, research methods, focus, genre, and expected outputs. Theoretical and practical implications of this critical analysis are outlined at the end of this paper, shedding light on the future directions of PSSG studies. the main conclusions of this study are as following: 1. An inter-disciplinary approach led by pedagogists is needed to design, develop, and examine PSSGs effectively. 2. The over-optimistic viewpoint about serious games in general and PSSGs in specific should be moderated by looking more closely at the negative marginal and side effects of implementing these games on gamers. [ABSTRACT FROM AUTHOR]

The released software systems still involve some faults, for which debugging becomes necessary. On the one hand, manual software debugging remains an arduous, time-consuming, and expensive task. On the other hand, effective software debugging is organized around motivated and patient developers. In this paper, a novel approach, namely Gamifault, is provided to make debugging more attractive and enjoyable. Particularly, the objective of Gamifault is to make the developer more curious to proceed debugging, that is fault localization and program repair, enthusiastically. To achieve this objective, the concepts and potentials of gamification are adapted to the typical tasks of software debugging. In particular, Gamifault makes use of an existing fault localization technique to determine the likelihood to each statement may be faulty. Based on the likelihood, the developer then attempts to find the exact fault location and fix the fault. Next, Gamifault reacts to the developer with a gamified success rate. That is, it shows the number of test cases that have been passed on the modified program. This process is repeated until the program passes on every given test case. To evaluate Gamifault, a prototype web-based tool was implemented in Java that targets faulty software programs. Then, 16 developers were asked to employ gamified and non-gamified versions of the tool in their debugging activities on 46 subject programs taken from the Code4Bench suite of programs. Developers could successfully debug 7 and 95 faulty programs using the non-gamified and gamified tools, respectively. In addition, the gamified tool helped developers debug the faulty program in less than two minutes on average. These results suggest that Gamifault offers advantages over existing debugging systems. [ABSTRACT FROM AUTHOR]

Spectrum-based fault localization (SFL) techniques have shown considerable effectiveness in localizing software faults. They leverage a ranking metric to automatically assign suspiciousness scores to certain entities in a given faulty program. However, for some programs, the current SFL ranking metrics lose effectiveness. In this paper, we introduce ConsilientSFL that is served to synthesize a new ranking metric for a given program, based on a customized combination of a set of given ranking metrics. ConsilientSFL can be significant since it demonstrates the usage of voting systems into a software engineering task. First, several mutated, faulty versions are generated for a program. Then, the mutated versions are executed with the test data. Next, the effectiveness of each existing ranking metric is computed for each mutated version. After that, for each mutated version, the computed existing metrics are ranked using a preferential voting system. Consequently, several top metrics are chosen based on their ranks across all mutated versions. Finally, the chosen ranking metrics are normalized and synthesized, yielding a new ranking metric. To evaluate ConsilientSFL, we have conducted experiments on 27 subject programs from Code4Bench and Siemens benchmarks. In the experiments, we found that ConsilientSFL outperformed every single ranking metric. In particular, for all programs on average, we have found performance measures recall, precision, f-measure, and percentage of code inspection, to be nearly 7, 9, 12, and 5 percentages larger than using single metrics, respectively. The impact of this work is twofold. First, it can mitigate the issue with the choice and usage of a proper ranking metric for the faulty program at hand. Second, it can help debuggers find more faults with less time and effort, yielding higher quality software. [ABSTRACT FROM AUTHOR]

Context: The use of registries to Coronary Artery Disease (CAD) data management plays an important role in the improvement of healthcare processes and reduction of outcomes for patients and healthcare providers. The present study aimed to compare the data management processes of CAD registries in the selected countries. Evidence Acquisition: This review study was conducted comparatively in 2019. After selecting countries based on some criteria, the required data were collected by searching valid databases, more useful search engines, and related websites to CAD registries for the selected countries as well as by sending E-mails containing a data extraction form to the related organizations. Results: Totally, five registries were chosen in the selected countries as follows: CADOSA (Australia), APPROACH (Canada), START (Italy), CLARIFY (Spain), and GWTG-CAD (US). The results showed that 60% of the selected registries made use of the electronic case report form for data gathering. The main data elements included demographic and general information, risk factors, vital sings, medication, laboratory tests results, examination results, ECG results, invasive measures and interventions, patient's status on discharge, results of follow-ups, and post-discharge outcomes. Conclusion: Developing CAD registries based on the data management principles provides the context to conduct cohort studies with very low costs. With regard to the study results, attention should be paid to data management processes, include data gathering, data processing, and information distribution, in development of CAD registries. [ABSTRACT FROM AUTHOR]

Software piracy has been known as unauthorised reconstruction or illegal redistribution of a licenced software. Detecting pirated from base software is a major concern since pirated software can lead to significant financial losses as well as serious security vulnerabilities. To detect software piracy, we have recently proposed Metamorphic Analysis for Automatic Software Piracy Detection (MetaSPD) with a proof-of-concept evaluation. The core idea of MetaSPD was inspired from metamorphic malware detection due to its similarity of software piracy detection. MetaSPD works primarily based on mining the opcode graph of the base software to extract micro-signatures. Then, it leverages a classifier model to decide whether a given suspicious file is a pirated version of the base software. This paper extends our prior work in several respects. First, we present a retrospective appraisal of the main literature aiming at laying bare the status quo of software piracy detection and arguments on the current problems of the field to motivate our work. We then elaborate on MetaSPD itself and the constituent components. We provide two extensive experiments to evident the effectiveness of MetaSPD. The experiments have been carried out on two different datasets. Each dataset comprises 1300 morphed variants of the respective base software that act as pirated versions of that software. We conducted our experiments using three different classifiers. The paper is also enriched with a detailed discussion of the different properties and concerns of MetaSPD. The results corroborate that an attacker, who is using a pirated version of the given software, can hardly hide illegal usage of the software even by applying superabundant obfuscations to the code. [ABSTRACT FROM AUTHOR]

In a critical environment, e.g. in a factory where an employee faces hazardous conditions, monitoring the health status of the employee is important. Thus, continuous connectivity of the employee to the network is the main concern of such networks. In this paper, we have proposed a decentralized approach for mobility management of mobile nodes in hazardous areas like factory. The proposed mobility structure for hazardous areas, called MSHA, organizes static nodes as a tree for an efficient routing, automatic addressing, and handling movement of mobile nodes. MSHA is capable of handling multiple failures of static nodes which disconnect a mobile node from the network. MSHA is highly scalable regarding the number of mobile nodes and the size of the covered monitoring area. The proposed scheme is evaluated based on different factors. The results reveal the superiority of MSHA compared with the previous works. The promising analytical results manifest the performance (about 20%) of MSHA specifically in reducing packet loss and hand-off delay caused by the failure of the static nodes. The performance does not degrade with increasing the number of mobile nodes. [ABSTRACT FROM AUTHOR]

In human-based computation, the machine outsources certain steps of an algorithm to humans to optimize computation by making an equilibrium of human and machine computation advantages. Software testing is an important part of the software development life cycle that aims to reveal failures in software. One of the most important activities in the software testing is test data generation. Although many automatic methods have been introduced to generate effective test data, humans are still extensively used in the software industry, due to the challenges of the proposed automatic methods. Assuming that software companies still need the help of humans, in this research, we hypothesize that a game with a purpose can be utilized to improve the process of human-based test data generation by making test data generation more cost-effective and fun. To investigate this hypothesis, we implemented a game with a purpose, Rings, to improve the process of human-based test data generation for program units. We also performed an experiment to evaluate this idea as entertainment as well as a mean for test data generation. The results show that by crowd-sourcing test data generation of program units to typical players of a game, we can make test data generation more cost-effective and fun. [ABSTRACT FROM AUTHOR]

The IOT infrastructure - IP-based mobile sensor network- makes it possible to provide two-directional communication between mobile sensors and the remote server. Mobility direction prediction is a major challenge in IP-based networks by which we can predict the next movement direction of moving objects carrying mobile node(s). In this paper, we have introduced a Distributed Self-Healing Movement Prediction scheme for IOT applications, so-called DSHMP-IOT, to predict movement direction of mobile IP-based sensors in a multi-user environment, such as a health-care system. This is the first time that an AI solution is applied to predict the direction of the mobile node(s) in an IP-based mobile network. The proposed scheme takes advantage of Hidden Semi-Markov Model (HSMM) to predict the movement direction with high accuracy and low overhead. The previous works for estimating the direction of a mobile node(s) in IP-based mobile networks are based on AOA, a hardware-specific method. The proposed scheme has several advantages. First, it eliminates the need for special hardware (directional antenna, an antenna array, etc.) which is required in AOA based methods. Second, it is not sensitive to noise, speed and sudden changing of movement direction which cause false positive movement direction prediction in AOA method. Third, in this context, it is the only work with self-healing capability whenever one or more static sensors fail(s). Fourth, it includes a recovery mechanism which prevents the mobile node from being disconnected in case of false prediction of our learning model. The simulation results show the superiority of our scheme regarding power consumption and hand-off delay, as well as packet loss, compared to similar approaches. [ABSTRACT FROM AUTHOR]

Considering the fact that faults may be revealed as undesired mutual effect of program predicates on each other, a new approach for localizing latent bugs, namely Hierarchy-Debug, is presented in this paper. To analyze the vertical effect of predicates on each other and on program termination status, the predicates are fitted into a logistic lasso model. To support scalability, a hierarchical clustering algorithm is applied to cluster the predicates according to their presence in different executions. Considering each cluster as a pseudo-predicate, a distinct lasso model is built for intermediate levels of the hierarchy. Then, we apply a majority voting technique to score the predicates according to their lasso coefficients at different levels of the hierarchy. The predicates with relatively higher scores are ranked as fault relevant predicates. To provide the context of failure, faulty sub-paths are identified as sequences of fault relevant predicates. The grouping effect of Hierarchy-Debug helps programmers to detect multiple bugs. Four case studies have been designed to evaluate the proposed approach on three well-known test suites, Space, Siemens, and Bash. The evaluations show that Hierarchy-Debug produces more precise results compared with prior fault localization techniques on the subject programs. [ABSTRACT FROM AUTHOR]