SimEvo: Testing Evolving Multi-process Software Systems

Yu, Tingting

doi:10.1109/icsme.2017.29

Cited by 3 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Software engineering is a branch of computer science that includes the design, development, evaluation, and implementation of computer software [37,38]. System testing and maintenance remains a key activity in software engineering that is concerned with error reporting and correction [39][40][41][42]. Academic software engineering projects are increasingly gaining attention as a reliable method to assess competency levels among ICT students [4,14,19].…”

Section: Introductionmentioning

confidence: 99%

Teamwork Distribution: Local vs. Global Software Engineering Project Development Teamwork

AL-Taharwa

2020

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Deliverable and course project become the preferred mean to measure learner competency and attainment of intended learning outcomes in IT-fields. Proper setup and evaluation for teamwork projects remains a key challenge for e-learning systems. This study investigates the possibility to improve the early prediction of academic software engineering project failure by treating teamwork differently according to the distribution of teamwork participants. Two configurations of teamwork distribution are considered. In the first configuration, a teamwork may include international participants, but all team participants are affiliated to the same institution, namely local teamwork. In the second configuration, a teamwork may include participants from different institutions, namely global teamwork. Software engineering projects are approached from two distinct perspectives. First, obeying the best practices during the system development life cycle (SDLC), namely, process perspective. Second, characteristics of the final deliverable deployed at each milestone of the SDLC, namely product perspective. A publicly released dataset collected by a designated e-learning environment is leveraged to validate the proposed approach. Results indicate a noticeable variance among local and global distributions. These results puts evidence that the reasons behind software engineering teamwork project failures may vary depending on the distribution of the teamwork, local vs. global. Consequently, it advise to customize e-learning systems differently according to the teamwork distribution.

show abstract

Section: Introductionmentioning

confidence: 99%

Teamwork Distribution: Local vs. Global Software Engineering Project Development Teamwork

AL-Taharwa

2020

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

Changing Software in a Changing World: How to Test in Presence of Variability, Adaptation and Evolution?

Bertolino

Inverardi

2019

From Software Engineering to Formal Methods and Tools, and Back

View full text Add to dashboard Cite

S EADS

Cai

et al. 2020

ACM Trans. Softw. Eng. Methodol.

View full text Add to dashboard Cite

Distributed software systems are increasingly developed and deployed today. Many of these systems are supposed to run continuously. Given their critical roles in our society and daily lives, assuring the quality of distributed systems is crucial. Analyzing runtime program dependencies has long been a fundamental technique underlying numerous tool support for software quality assurance. Yet conventional approaches to dynamic dependence analysis face severe scalability barriers when they are applied to real-world distributed systems, due to the unbounded executions to be analyzed in addition to common efficiency challenges suffered by dynamic analysis in general. In this article, we present S EADS , a distributed , online , and cost-effective dynamic dependence analysis framework that aims at scaling the analysis to real-world distributed systems. The analysis itself is distributed to exploit the distributed computing resources (e.g., a cluster) of the system under analysis; it works online to overcome the problem with unbounded execution traces while running continuously with the system being analyzed to provide timely querying of analysis results (i.e., runtime dependence set of any given query). Most importantly, given a user-specified time budget, the analysis automatically adjusts itself to better cost-effectiveness tradeoffs (than otherwise) while respecting the budget by changing various analysis parameters according to the time being spent by the dependence analysis. At the core of the automatic adjustment is our application of a reinforcement learning method for the decision making—deciding which configuration to adjust to according to the current configuration and its associated analysis cost with respect to the user budget. We have implemented S EADS for Java and applied it to eight real-world distributed systems with continuous executions. Our empirical results revealed the efficiency and scalability advantages of our framework over a conventional dynamic analysis, at least for dynamic dependence computation at method level. While we demonstrate it in the context of dynamic dependence analysis in this article, the methodology for achieving and maintaining scalability and greater cost-effectiveness against continuously running systems is more broadly applicable to other dynamic analyses.

show abstract

SimEvo: Testing Evolving Multi-process Software Systems

Cited by 3 publications

References 45 publications

Teamwork Distribution: Local vs. Global Software Engineering Project Development Teamwork

Teamwork Distribution: Local vs. Global Software Engineering Project Development Teamwork

Changing Software in a Changing World: How to Test in Presence of Variability, Adaptation and Evolution?

S EADS

Contact Info

Product

Resources

About