Software Project Management Approaches for Global Software Development： A Systematic Mapping Study

Global Software Development （GSD） is a well established field of software engineering with the benefits of a global environment. Software Project Management （SPM） plays a key role in the success of GSD. As a result, the need has arisen to study and evaluate the downsides of SPM for GSD, to thereby pave the way for the development of new methods, techniques, and tools with which to tackle them. This paper aims to identify and classify research on SPM approaches for GSD that are available in the literature, to identify their current weaknesses and strengths, and to analyze their applications in industry. We performed a Systematic Mapping Study （SMS） based on six classification criteria. Eighty-four papers were selected and analyzed. The results indicate that interest in SPM for GSD has been increasing since 2006. As a class of approaches, the most frequently reported methods （40%） are those used for coordination, planning, and monitoring, along with estimation techniques that can be used to better match a distributed project. SPM for GSD requires further investigation by researchers and practitioners, particularly with respect to cost and time estimations. These findings will help overcome the challenges that must to be considered in future SPM research for GSD, especially regarding collaboration and time-zone differences.

Migration of existing software systems to mobile computing platforms:a systematic mapping study

Mobile computing has fast emerged as a pervasive technology to replace the old computing paradigms with portable computation and context-aware communication.Existing software systems can be migrated(while preserving their data and logic)to mobile computing platforms that support portability,context-sensitivity,and enhanced usability.In recent years,some research and development efforts have focused on a systematic migration of existing software systems to mobile computing platforms.To investigate the research state-of-the-art on the migration of existing software systems to mobile computing platforms.We aim to analyze the progression and impacts of existing research,highlight challenges and solutions that reflect dimensions of emerging and futuristic research.We followed evidence-based software engineering(EBSE)method to conduct a systematic mapping study(SMS)of the existing research that has progressed over more than a decade(25 studies published from 1996–2017).We have derived a taxonomical classification and a holistic mapping of the existing research to investigate its progress,impacts,and potential areas of futuristic research and development.The SMS has identified three types of migration namely Static,Dynamic,and State-based Migration of existing software systems to mobile computing platforms.Migration to mobile computing platforms enables existing software systems to achieve portability,context-sensitivity,and high connectivity.However,mobile systems may face some challenges such as resource poverty,data security,and privacy.The emerging and futuristic research aims to support patterns and tool support to automate the migration process.The results of this SMS can benefit researchers and practitioners-by highlighting challenges,solutions,and tools,etc.,-to conceptualize the state-of-the-art and futuristic trends that support migration of existing software to mobile computing.

Curvature Motion on Dual Hyperbolic Unit Sphere H^(2)_(0)

In this paper, we define dual curvature motion on the dual hyperbolic unit sphere H2<sub style="margin-left:-8px;">0 in the dual Lorentzian space D31 with dual signature (+,+-) . We carry the obtained results to the Lorentzian line space R3<sub style="margin-left:-8px;">1 by means of Study mapping. Then we make an analysis of the orbits during the dual hyperbolic spherical curvature motion. Finally, we find some line congruences, the family of ruled surfaces and ruled surfaces in R3<sub style="margin-left:-8px;">1.

Empirical Research in Software Engineering -- A Literature Survey

Empirical research is playing a significant role in software engineering （SE）, and it has been applied to evaluate software artifacts and technologies. There have been a great number of empirical research articles published recently. There is also a large research community in empirical software engineering （ESE）. In this paper, we identify both the overall landscape and detailed implementations of ESE, and investigate frequently applied empirical methods, targeted research purposes, used data sources, and applied data processing approaches and tools in ESE. The aim is to identify new trends and obtain interesting observations of empirical software engineering across different sub-fields of software engineering. We conduct a mapping study on 538 selected articles from January 2013 to November 2017, with four research questions. We observe that the trend of applying empirical methods in software engineering is continuously increasing and the most commonly applied methods are experiment, case study and survey. Moreover, open source projects are the most frequently used data sources. We also observe that most of researchers have paid attention to the validity and the possibility to replicate their studies. These observations are carefully analyzed and presented as carefully designed diagrams. We also reveal shortcomings and demanded knowledge/strategies in ESE and propose recommendations for researchers.

Transcriptome-wide association studies: a view from Mendelian randomization

Background:Genome-wide association studies(GWASs)have identified thousands of genetic variants that are associated with many complex traits.However,their biological mechanisms remain largely unknown.Transcriptome-wide association studies(TWAS)have been recently proposed as an invaluable tool for investigating the potential gene regulatory mechanisms underlying variant-trait associations.Specifically,TWAS integrate GWAS with expression mapping studies based on a common set of variants and aim to identify genes whose GReX is associated with the phenotype.Various methods have been developed for performing TWAS and/or similar integrative analysis.Each such method has a different modeling assumption and many were initially developed to answer different biological questions.Consequently,it is not straightforward to understand their modeling property from a theoretical perspective.Results:We present a technical review on thirteen TWAS methods.Importantly,we show that these methods can all be viewed as two-sample Mendelian randomization(MR)analysis,which has been widely applied in GWASs for examining the causal effects of exposure on outcome.Viewing different TWAS methods from an MR perspective provides us a unique angle for understanding their benefits and pitfalls.We systematically introduce the MR analysis framework,explain how features of the GWAS and expression data influence the adaptation of MR for TWAS,and re-interpret the modeling assumptions made in different TWAS methods from an MR angle.We finally describe future directions for TWAS methodology development.Conclusions:We hope that this review would serve as a useful reference for both methodologists who develop TWAS methods and practitioners who perform TWAS analysis.