MEASURING AND QUANTIFYING WEB APPLICATION DESIGN

The World Wide Web makes it easier for organizations and people to share information across great distances at a minimal cost. In addition, governments and businesses are beginning to offer services on the World Wide Web to their respective populations and customers. Unlike traditional desktop based applications where the programming language used are most likely Object Oriented (OO) based languages such as C++ or Java, most web applications are built upon more lightweight scripting languages. These languages typically don’t support all the OO features that more traditional languages support. In addition, some web applications are dependent on external web services or applications. These differences make it difficult to use traditional measuring techniques such as Quality Measurements in Object Oriented Design to quantify the complexity of a web application or its quality. This paper will propose a set of measurements derived from traditional Object Oriented metrics such as QMOOD, and attempt to use them on two Content Management Systems; Drupal and WordPress. These measurements will try to quantify the size and complexity of the two content management systems and make comparisons between them

Effective Compiler Error Message Enhancement for Novice Programming Students

Programming is an essential skill that all computing students must master. However programming can be difficult to learn. Compiler error messages are crucial for correcting errors, but are often difficult to understand and pose a barrier to progress for many novices. High frequencies of errors, particularly repeated errors, have been shown to be indicators of students who are struggling with learning to program. This study involves a custom IDE that enhances Java compiler error messages, intended to be more useful to novices than those supplied by the compiler. The effectiveness of this approach was tested in an empirical control/intervention study of approximately 200 students generating almost 50,000 errors. The design allows for direct comparisons between enhanced and non-enhanced error messages. Results show that the intervention group experienced reductions in the number of overall errors, errors per student, and several repeated error metrics. This work is important for two reasons. First, the effects of error message enhancement have been recently debated in the literature. This study provides substantial evidence that it can be effective. Second, these results should be generalizable at least in part, to other programming languages, students and institutions, as we show that the control group of this study is comparable to several others using Java and other languages

The System Kato: Detecting Cases of Plagiarism for Answer-Set Programs

Plagiarism detection is a growing need among educational institutions and solutions for different purposes exist. An important field in this direction is detecting cases of source-code plagiarism. In this paper, we present the tool Kato for supporting the detection of this kind of plagiarism in the area of answer-set programming (ASP). Currently, the tool is implemented for DLV programs but it is designed to handle other logic-programming dialects as well. We review the basic features of Kato, introduce its theoretical underpinnings, and discuss an application of Kato for plagiarism detection in the context of courses on logic programming at the Vienna University of Technology

Evaluating Maintainability Prejudices with a Large-Scale Study of Open-Source Projects

Exaggeration or context changes can render maintainability experience into prejudice. For example, JavaScript is often seen as least elegant language and hence of lowest maintainability. Such prejudice should not guide decisions without prior empirical validation. We formulated 10 hypotheses about maintainability based on prejudices and test them in a large set of open-source projects (6,897 GitHub repositories, 402 million lines, 5 programming languages). We operationalize maintainability with five static analysis metrics. We found that JavaScript code is not worse than other code, Java code shows higher maintainability than C# code and C code has longer methods than other code. The quality of interface documentation is better in Java code than in other code. Code developed by teams is not of higher and large code bases not of lower maintainability. Projects with high maintainability are not more popular or more often forked. Overall, most hypotheses are not supported by open-source data.Comment: 20 page