Novel Software Hybrid Testing Model by Using Trusted Computing Group Technology

Software testing is a procedure that includes executing a program or application while checking for any errors or bugs in order to produce software that is free of defects. Only testing (software testing) can determine the quality of any software. Worldwide technological advancements have resulted to a development in the number of verification techniques and methodologies available for testing software before it goes to production and possibly makes its way into the market. Thus, automation testing has impacted the testing procedure. Automation tools are used for software testing, which not only minimizes the number of people using the application but also reduces the possibility of errors even with testers testing. Therefore, to increase the efficiency of testing this hybrid model is described. So, by using this testing errors can be detected accurately. This novel software hybrid testing model by using Trusted Computing Group (TCG) technology shows accurate result while testing the software. Hence, this model shows better results in terms of accuracy, time and precision

Application of Constructive Modeling and Process Mining Approaches to the Study of Source Code Development in Software Engineering Courses

We present an approach of constructing a source code history for a modern code review. Practically, it is supposed to be used in programming training, especially within initial stages. The developed constructor uses constructive-synthesizing modeling tools to classify a source code history by fine-grained changes and to construct an event log file aimed to provide information on students’ coding process. Current research applies Process Mining techniques to the software engineering domain to identify software engineering skills. By better understanding of the way students design programs, we will help novices learn programming. This research provides an innovative method of using code and development process review in teaching programming skills and is aimed to encourage using code review and monitoring coding practice in educational purposes. The standard method of evaluation takes into consideration only a final result, which doesn’t meet modern requirements of teaching programming

Critical Success Factors of Continuous Practices in a DevOps Context

Context: Software companies try to achieve adaptive near to real-time software delivery and apply continuous practices in a DevOps context. While continuous practices may create new business opportunities, continuous practices also present new challenges. Objective: This study aims to aid in adopting continuous practices and performance improvements by increasing our understanding of these practices in a DevOps context. Method: By conducting a systematic literature review we identified critical success factors on continuous practices and grouped the found factors. This led to the construction of our initial framework. We started to validate the critical success factors in this framework in a DevOps context by conducting a first pilot interview. Results: We developed an initial framework of critical success factors and conducted a pilot interview to make a first step to validate the framework. Some factors were confirmed and clarified i.e., enriched, on the basis of the retrieved information. In future work we will strive at further validation of the framework. Conclusions: We took a first step to validate our framework and retrieved valuable information, which is promising to take the next steps for further development of the framework

E-Mentoring in Higher Education: A Structured Literature Review and Implications for Future Research

[EN]Mentoring in higher education helps learners acclimate to a new academic topic, increases the likelihood of academic success, and reduces attrition. Learners rely on the expertise and experience of mentors to help them graduate in a timely manner and advance on to their career. As online and distance education becomes more pervasive, computer-mediated mentoring allows learners to connect with their mentors in new ways. Research about mentoring in higher education includes investigations into the e cacy of virtual or e-mentoring. We conducted a literature review of research from 2009 to 2019 to identify relevant elements for implementing e-mentoring programs in higher education. Our research revealed that there is a consistent interest in the subject matter within educational research; however, there is a gap on virtual mentoring in higher education for students conducting o site internships. Our research reviews e-mentoring programs, identifies how these programs are evaluated, identifies factors of successful programs, and establishes a research agenda in areas of e-mentoring programs for students in o site internships and how they can be structured to achieve the same level of success

On Reducing the Energy Consumption of Software: From Hurdles to Requirements

International audienceBackground. As software took control over hardware in many domains, the question of the energy footprint induced by the software is becoming critical for our society, as the resources powering the underlying infrastructure are finite. Yet, beyond this growing interest, energy consumption remains a difficult concept to master for a developer.Aims. The purpose of this study is to better understand the root causes that prevent software energy consumption to be more widely adopted by developers and companies.Method. To investigate this issue, this paper reports on a qualitative study we conducted in an industrial context. We applied an in-depth analysis of the interviews of 10 experienced developers and summarized a set of implications.Results. We argue that our study delivers i) insightful feedback on how green software design is considered among the interviewed developers and ii) a set of findings to build helpful tools, motivate further research, and establish better development strategies to promote green software design.Conclusion. This paper covers an industrial case study of developers' awareness of green software design and how to promote it within the company. While it might not be generalizable for any company, we believe our results deliver a common body of knowledge with implications to be considered for similar cases and further researches

Characterizing Deep Learning Package Supply Chains in PyPI: Domains, Clusters, and Disengagement

Deep learning (DL) package supply chains (SCs) are critical for DL frameworks to remain competitive. However, vital knowledge on the nature of DL package SCs is still lacking. In this paper, we explore the domains, clusters, and disengagement of packages in two representative PyPI DL package SCs to bridge this knowledge gap. We analyze the metadata of nearly six million PyPI package distributions and construct version-sensitive SCs for two popular DL frameworks: TensorFlow and PyTorch. We find that popular packages (measured by the number of monthly downloads) in the two SCs cover 34 domains belonging to eight categories. Applications, Infrastructure, and Sciences categories account for over 85% of popular packages in either SC and TensorFlow and PyTorch SC have developed specializations on Infrastructure and Applications packages respectively. We employ the Leiden community detection algorithm and detect 131 and 100 clusters in the two SCs. The clusters mainly exhibit four shapes: Arrow, Star, Tree, and Forest with increasing dependency complexity. Most clusters are Arrow or Star, but Tree and Forest clusters account for most packages (Tensorflow SC: 70%, PyTorch SC: 90%). We identify three groups of reasons why packages disengage from the SC (i.e., remove the DL framework and its dependents from their installation dependencies): dependency issues, functional improvements, and ease of installation. The most common disengagement reason in the two SCs are different. Our study provides rich implications on the maintenance and dependency management practices of PyPI DL SCs.Comment: Manuscript submitted to ACM Transactions on Software Engineering and Methodolog

XP2021 Experience Report: Five Strategies for the Future of Work: Accelerating Innovation through Tech Transfer

This experience report outlines five tech transfer strategies developed over a period of 25 years at four Global 1000 companies (HP, Cisco, Qualcomm, and Nortel) to mitigate R&D challenges associated with duplicated effort, product quality, and time-to-market. The five strategies accelerate innovation through open knowledge sharing, rather than licensing intellectual property rights (IPR) such as patents, trade secrets, and copyrights. The strategies are based on corporate tech forums, conference panels, exploratory workshops, research reviews (at universities and companies), and talent exchanges. While the initial objective was to foster the corporate adoption of software best practices, over time the strategies had broader impact on company innovation, including incubating cross-company R&D collaborations, capturing organizational memory, cultivating and leveraging external research partnerships, and feeding company talent pipelines