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With the advent of numerous languages it is difficult to realize the edge of one language in a particular scope over another one. We are making an effort, realizing these few issues and comparing some main stream languages like Java, Scala, C++, Haskell, VB .NET, AspectJ, Perl, Ruby, PHP and Scheme keeping in mind some core issues in program development.

With the advent of numerous languages it is difficult to realize the edge of one language in a particular scope over another one. We are making an effort, realizing these few issues and comparing some main stream languages like Java, Scala, C++, Haskell, VB .NET, AspectJ, Perl, Ruby, PHP and Scheme keeping in mind some core issues in program development.

Concurrency has been rapidly gaining importance in general-purpose computing, caused by the recent turn towards multicore processing architectures. As a result, an increasing number of developers have to learn to write concurrent programs, a task that is known to be hard even for the expert. Language designers are therefore working on languages that promise to make concurrent programming "easier" than using traditional thread libraries. However, the claim that a new language is more usable than another cannot be supported by purely theoretical considerations, but calls for empirical studies. In this paper, we present the design of a study to compare concurrent programming languages with respect to comprehending and debugging existing programs and writing correct new programs. A critical challenge for such a study is avoiding the bias that might be introduced during the training phase and when interpreting participants' solutions. We address these issues by the use of self-study material and an evaluation scheme that exposes any subjective decisions of the corrector, or eliminates them altogether. We apply our design to a comparison of two object-oriented languages for concurrency, multithreaded Java and SCOOP (Simple Concurrent Object-Oriented Programming), in an academic setting. We obtain results in favor of SCOOP even though the study participants had previous training in Java Threads.

Concurrency has been rapidly gaining importance in general-purpose computing, caused by the recent turn towards multicore processing architectures. As a result, an increasing number of developers have to learn to write concurrent programs, a task that is known to be hard even for the expert. Language designers are therefore working on languages that promise to make concurrent programming "easier" than using traditional thread libraries. However, the claim that a new language is more usable than another cannot be supported by purely theoretical considerations, but calls for empirical studies. In this paper, we present the design of a study to compare concurrent programming languages with respect to comprehending and debugging existing programs and writing correct new programs. A critical challenge for such a study is avoiding the bias that might be introduced during the training phase and when interpreting participants' solutions. We address these issues by the use of self-study material and an evaluation scheme that exposes any subjective decisions of the corrector, or eliminates them altogether. We apply our design to a comparison of two object-oriented languages for concurrency, multithreaded Java and SCOOP (Simple Concurrent Object-Oriented Programming), in an academic setting. We obtain results in favor of SCOOP even though the study participants had previous training in Java Threads.

Concurrency has been rapidly gaining importance in general-purpose computing, caused by the recent turn towards multicore processing architectures. As a result, an increasing number of developers have to learn to write concurrent programs, a task that is known to be hard even for the expert. Language designers are therefore working on languages that promise to make concurrent programming "easier" than using traditional thread libraries. However, the claim that a new language is more usable than another cannot be supported by purely theoretical considerations, but calls for empirical studies. In this paper, we present the design of a study to compare concurrent programming languages with respect to comprehending and debugging existing programs and writing correct new programs. A critical challenge for such a study is avoiding the bias that might be introduced during the training phase and when interpreting participants' solutions. We address these issues by the use of self-study material and an evaluation scheme that exposes any subjective decisions of the corrector, or eliminates them altogether. We apply our design to a comparison of two object-oriented languages for concurrency, multithreaded Java and SCOOP (Simple Concurrent Object-Oriented Programming), in an academic setting. We obtain results in favor of SCOOP even though the study participants had previous training in Java Threads.

Sometimes debates on programming languages are more religious than scientific. Questions about which language is more succinct or efficient, or makes developers more productive are discussed with fervor, and their answers are too often based on anecdotes and unsubstantiated beliefs. In this study, we use the largely untapped research potential of Rosetta Code, a code repository of solutions to common programming tasks in various languages, to draw a fair and well-founded comparison. Rosetta Code offers a large data set for analysis. Our study is based on 7087 solution programs corresponding to 745 tasks in 8 widely used languages representing the major programming paradigms (procedural: C and Go; object-oriented: C# and Java; functional: F# and Haskell; scripting: Python and Ruby). Our statistical analysis reveals, most notably, that: functional and scripting languages are more concise than procedural and object-oriented languages; C is hard to beat when it comes to raw speed on large inputs, but performance differences over inputs of moderate size are less pronounced and allow even interpreted languages to be competitive; compiled strongly-typed languages, where more defects can be caught at compile time, are less prone to runtime failures than interpreted or weakly-typed languages. We discuss implications of these results for developers, language designers, and educators.