Java programming paradigm comprehensibility: Proceduralversus Reactive

Software developers spend more time on reading than writingcode. Comprehensible code therefore has the potential tosignificantly improve software development andmaintenance by lowering the time needed for understandingexisting code. Previous research suggest that the choice ofProgramming paradigm may affect code comprehension. Thepresent study evaluates if a positive effect of ReactiveProgramming on comprehension can be attested incomparison to Procedural Programming. We let human testsubjects solve bugs in code-snippets of commonfunctionalities implemented either according to ReactiveProgramming or Procedural Programming in the Javalanguage and RxJava, its ReactiveX implementation. Thecomprehensibility of the code is measured by the test subjects’time consumption, with lower values indicating highercomprehensibility and higher values lowercomprehensibility.Within this study we also study the effect of prior knowledgeof reactive programming, and background in programming,on the results, by having test subjects from two groups: (1)software students with experience in Reactive Programming,and (2) experienced software developers and engineers withless experience in Reactive Programming.All tests took part in a tool of our design, the CodeComparator.Our results show that reactive puzzles are solved faster,suggesting higher comprehensibility, although highdispersion in solvability time, especially for the proceduralsolutions, make it difficult to assess the validity of this timedifference. The positive effect is notable in the student groupwhereas we cannot conclude if the other group solves reactiveor procedural puzzles faste

Java programming paradigm comprehensibility: Proceduralversus Reactive

Software developers spend more time on reading than writingcode. Comprehensible code therefore has the potential tosignificantly improve software development andmaintenance by lowering the time needed for understandingexisting code. Previous research suggest that the choice ofProgramming paradigm may affect code comprehension. Thepresent study evaluates if a positive effect of ReactiveProgramming on comprehension can be attested incomparison to Procedural Programming. We let human testsubjects solve bugs in code-snippets of commonfunctionalities implemented either according to ReactiveProgramming or Procedural Programming in the Javalanguage and RxJava, its ReactiveX implementation. Thecomprehensibility of the code is measured by the test subjects’time consumption, with lower values indicating highercomprehensibility and higher values lowercomprehensibility.Within this study we also study the effect of prior knowledgeof reactive programming, and background in programming,on the results, by having test subjects from two groups: (1)software students with experience in Reactive Programming,and (2) experienced software developers and engineers withless experience in Reactive Programming.All tests took part in a tool of our design, the CodeComparator.Our results show that reactive puzzles are solved faster,suggesting higher comprehensibility, although highdispersion in solvability time, especially for the proceduralsolutions, make it difficult to assess the validity of this timedifference. The positive effect is notable in the student groupwhereas we cannot conclude if the other group solves reactiveor procedural puzzles faste

Neonatal immune-tolerance in mice does not prevent xenograft rejection

Assessing the efficacy of human stem cell transplantation in rodent models is complicated by the significant immune rejection that occurs. Two recent reports have shown conflicting results using neonatal tolerance to xenografts in rats. Here we extend this approach to mice and assess whether neonatal tolerance can prevent the rapid rejection of xenografts. In three strains of neonatal immune-intact mice, using two different brain transplant regimes and three independent stem cell types, we conclusively show that there is rapid rejection of the implanted cells. We also address specific challenges associated with the generation of humanized mouse models of disease