6 results
Search Results
Now showing 1 - 6 of 6
Editorial Citation - Scopus: 1Quality and Reliability Engineering: Trends and Future Directions(Graz Univ Technolgoy, inst information Systems Computer Media-iicm, 2018) Mishra, Alok; Khatri, Sunil Kumar; Kapur, P. K.; Kumar, Uday; Software Engineering[No Abstract Available]Article Citation - WoS: 7Citation - Scopus: 8A Global Software Inspection Process for Distributed Software Development(Graz Univ Technolgoy, inst information Systems Computer Media-iicm, 2012) Mishra, Deepti; Mishra, Alok; Computer Engineering; Software EngineeringGlobally distributed software development is an established trend towards delivering high-quality software to global users at lower costs. The main expected benefits from distributed software development are improvements in development time efficiency, being close to the customers and having flexible access to greater and less costly resources. Organizations require to use their existing resources as effectively as possible, and also need to employ resources on a global scale from different sites within the organization and from partner organization throughout the world. However, distributed software development particularly face communication and coordination problems due to spatial, temporal and cultural separation between team members. Ensuring quality issues in such projects is a significant issue. This paper presents global software inspection process in the distributed software development environment towards quality assurance and management.Article Citation - WoS: 1Citation - Scopus: 1Statistical Usage Testing at Different Levels of Testing(Graz Univ Technolgoy, inst information Systems Computer Media-iicm, 2018) Kaur, Kamaldeep; Khatri, Sunil Kumar; Mishra, Alok; Datta, Rattan; Software EngineeringStatistical Usage Testing (SUT) is the testing technique defined in Cleanroom Software Engineering model [Runeson, 93]. Cleanroom Software Engineering model is a theory based and team oriented model that is based on development and certification of software in increments using statistical quality control [Linger 96]. SUT is a black box testing technique and concentrates on how the software completes its required function from the user's perspective [Runeson, 93]. SUT is carried out by developing usage models and assigning usage probabilities. Testing is carried out on usage models by performing statistical tests which are random sequences [Trammel 95]. Statistical testing can be viewed as a statistical experiment where random test cases are selected from all the usage models [Trammel 95]. This paper demonstrates the process and benefits of applying SUT at different levels of testing. Levels of testing include Unit level, Integration level, System level and Acceptance level. SUT is generally performed at System level and Unit testing is not the part of SUT. Unit testing makes it easier to access code and debug human errors. Detecting errors at an early stage helps reducing cost and effort. The paper proposes to allow Unit testing in Cleanroom Software Engineering Model, thus making it more flexible and suitable for varied applications. Unit testing is essentially performed to ensure that the code is working correctly and meets the user specifications [istqb, 15]. Errors may also exist when modules are integrated because of interchange of data and control information between various modules. Integration testing is performed when the modules are combined together to check their behaviour and functionality after integration. Once the Integration testing phase gets successfully completed, System testing is performed on the whole system [test-institute, 15]. The paper makes use of Student record software to demonstrate the process of performing SUT at different levels. In addition to performing SUT at System level, this paper helps in understanding the advantages of applying SUT at Unit level and Integration level.Article Citation - WoS: 10Citation - Scopus: 15Software Quality Issues in SCRUM: A Systematic Mapping(Graz Univ Technolgoy, inst information Systems Computer Media-iicm, 2018) Mishra, Deepti; Abdalhamid, Samia; Computer EngineeringScrum is a process framework used to develop complex software. As Scrum is one of the prominent approaches in agile development projects, it is significant to define the issues of quality in the Scrum method. In this paper, a systematic mapping approach is adopted to answer specific research questions through an objective procedure to identify the nature of quality issues in Scrum studies. For this purpose, a number of research studies are reviewed in electronic databases to find out about various quality issues related with Scrum. Here, the focus is on how these studies are affective in terms of defining such issues. A total of 53 research papers are examined in detail to answer nine research questions related to quality issues in Scrum. Finally, the responses to all research questions are provided along with suggestions to ensure quality in the Scrum. The results reveal that there is very limited research on people-related quality issues such as employee skills, satisfaction etc. However, process quality such as process effectiveness, conformity, visibility, acceptance etc. have received a lot of attention among researchers, whereas the product quality and project-related quality issues such as team performance, collaboration, etc. are also of interest among researchers.Article Citation - WoS: 17Citation - Scopus: 30Comparative Study of Real Time Machine Learning Models for Stock Prediction Through Streaming Data(Graz Univ Technolgoy, inst information Systems Computer Media-iicm, 2020) Behera, Ranjan Kumar; Das, Sushree; Rath, Santanu Kumar; Misra, Sanjay; Damasevicius, Robertas; Computer EngineeringStock prediction is one of the emerging applications in the field of data science which help the companies to make better decision strategy. Machine learning models play a vital role in the field of prediction. In this paper, we have proposed various machine learning models which predicts the stock price from the real-time streaming data. Streaming data has been a potential source for real-time prediction which deals with continuous flow of data having information from various sources like social networking websites, server logs, mobile phone applications, trading floors etc. We have adopted the distributed platform, Spark to analyze the streaming data collected from two different sources as represented in two case studies in this paper. The first case study is based on stock prediction from the historical data collected from Google finance websites through NodeJs and the second one is based on the sentiment analysis of Twitter collected through Twitter API available in Stanford NLP package. Several researches have been made in developing models for stock prediction based on static data. In this work, an effort has been made to develop scalable, fault tolerant models for stock prediction from the real-time streaming data. The Proposed model is based on a distributed architecture known as Lambda architecture. The extensive comparison is made between actual and predicted output for different machine learning models. Support vector regression is found to have better accuracy as compared to other models. The historical data is considered as a ground truth data for validation.Editorial Citation - WoS: 3Citation - Scopus: 3Distributed Development of Information System J.ucs Special Issue(Graz Univ Technolgoy, inst information Systems Computer Media-iicm, 2012) Mishra, Alok; Munch, Jurgen; Mishra, Deepti; Computer Engineering; Software Engineering[No Abstract Available]
