Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Lightweight Approach of Human-Like Playtesting
52
0
0.0
(
0
)
Added by
Yan Zhao
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
A playtest is the process in which human testers are recruited to play video games and to reveal software bugs. Manual testing is expensive and time-consuming, especially when there are many mobile games to test and every software version requires for extensive testing before being released. Existing testing frameworks (e.g., Android Monkey) are limited because they adopt no domain knowledge to play games. Learning-based tools (e.g., Wuji) involve a huge amount of training data and computation before testing any game. This paper presents LIT -- our lightweight approach to generalize playtesting tactics from manual testing, and to adopt the generalized tactics to automate game testing. LIT consists of two phases. In Phase I, while a human plays an Android game app G for a short period of time (e.g., eight minutes), tool records the users actions (e.g., swipe) and the scene before each action. Based on the collected data, LIT generalizes a set of emph{context-aware, abstract playtesting tactics} which describe under what circumstances, what actions can be taken to play the game. In Phase II, LIT tests G based on the generalized tactics. Namely, given a randomly generated game scene, LIT searches match for the abstract context of any inferred tactic; if there is a match, LIT customizes the tactic and generates a feasible event to play the game. Our evaluation with nine games shows LIT to outperform two state-of-the-art tools. This implies that by automating playtest, LIT will significantly reduce manual testing and boost the quality of game apps.
rate research
Read More
Automated testing tools typically create test cases that are different from what human testers create. This often makes the tools less effective, the created tests harder to understand, and thus results in tools providing less support to human testers. Here, we propose a framework based on cognitive science and, in particular, an analysis of approaches to problem-solving, for identifying cognitive processes of testers. The framework helps map test design steps and criteria used in human test activities and thus to better understand how effective human testers perform their tasks. Ultimately, our goal is to be able to mimic how humans create test cases and thus to design more human-like automated test generation systems. We posit that such systems can better augment and support testers in a way that is meaningful to them.
One of the more prominent trends within Industry 4.0 is the drive to employ Robotic Process Automation (RPA), especially as one of the elements of the Lean approach. The full implementation of RPA is riddled with challenges relating both to the reality of everyday business operations, from SMEs to SSCs and beyond, and the social effects of the changing job market. To successfully address these points there is a need to develop a solution that would adjust to the existing business operations and at the same time lower the negative social impact of the automation process. To achieve these goals we propose a hybrid, human-centered approach to the development of software robots. This design and implementation method combines the Living Lab approach with empowerment through participatory design to kick-start the co-development and co-maintenance of hybrid software robots which, supported by variety of AI methods and tools, including interactive and collaborative ML in the cloud, transform menial job posts into higher-skilled positions, allowing former employees to stay on as robot co-designers and maintainers, i.e. as co-programmers who supervise the machine learning processes with the use of tailored high-level RPA Domain Specific Languages (DSLs) to adjust the functioning of the robots and maintain operational flexibility.
Among formal methods, the deductive verification approach allows establishing the strongest possible formal guarantees on critical software. The downside is the cost in terms of human effort required to design adequate formal specifications and to successfully discharge the required proof obligations. To popularize deductive verification in an industrial software development environment, it is essential to provide means to progressively transition from simple and automated approaches to deductive verification. The SPARK environment, for development of critical software written in Ada, goes towards this goal by providing automated tools for formally proving that some code fulfills the requirements expressed in Ada contracts. In a program verifier that makes use of automatic provers to discharge the proof obligations, a need for some additional user interaction with proof tasks shows up: either to help analyzing the reason of a proof failure or, ultimately, to discharge the verification conditions that are out-of-reach of state-of-the-art automatic provers. Adding interactive proof features in SPARK appears to be complicated by the fact that the proof toolchain makes use of the independent, intermediate verification tool Why3, which is generic enough to accept multiple front-ends for different input languages. This paper reports on our approach to extend Why3 with interactive proof features and also with a generic client-server infrastructure allowing integration of proof interaction into an external, front-end graphical user interface such as the one of SPARK.
In Software Engineering, some of the most critical activities are maintenance and evolution. However, to perform both with quality, minimizing impacts and risks, developers need to analyze and identify where the main problems come from previously. In this paper, we introduce DR-Tools Suite, a set of lightweight open-source tools that analyze and calculate source code metrics, allowing developers to visualize the results in different formats and graphs. Also, we define a set of heuristics to help the code analysis. We conducted two case studies (one academic and one industrial) to collect feedback on the tools suite, on how we will evolve the tools, as well as insights to develop new tools that support developers in their daily work.
The paper presents the results of a behavioral experiment conducted between February 2020 and March 2021 at Universit`a Cattolica del Sacro Cuore, Milan Campus in which students were matched to either a human or a humanoid robotic partner to play an iterated Prisoners Dilemma. The results of a Logit estimation procedure show that subjects are more likely to cooperate with human rather robotic partners; that are more likely to cooperate after receiving a dialogic verbal reaction following the realization of a sub-obtimal social outcome; that the effect of the verbal reaction is independent on the nature of the partner. Our findings provide new evidence on the effect of verbal communication in strategic frameworks. Results are robust to the exclusion of students of Economics related subjects, to the inclusion of a set of psychological and behavioral controls, to the way subjects perceive robots behavior and to potential gender biases in human-human interactions.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
