FOAL 2004 Proceedings: Foundations of Aspect-Oriented Languages Workshop at AOSD 2004

Aspect-oriented programming is a paradigm in software engineering and FOAL logos courtesy of Luca Cardelli programming languages that promises better support for separation of concerns. The third Foundations of Aspect-Oriented Languages (FOAL) workshop was held at the Third International Conference on Aspect-Oriented Software Development in Lancaster, UK, on March 23, 2004. This workshop was designed to be a forum for research in formal foundations of aspect-oriented programming languages. The call for papers announced the areas of interest for FOAL as including, but not limited to: semantics of aspect-oriented languages, specification and verification for such languages, type systems, static analysis, theory of testing, theory of aspect composition, and theory of aspect translation (compilation) and rewriting. The call for papers welcomed all theoretical and foundational studies of foundations of aspect-oriented languages. The goals of this FOAL workshop were to: � Make progress on the foundations of aspect-oriented programming languages. � Exchange ideas about semantics and formal methods for aspect-oriented programming languages. � Foster interest within the programming language theory and types communities in aspect-oriented programming languages. � Foster interest within the formal methods community in aspect-oriented programming and the problems of reasoning about aspect-oriented programs. The papers at the workshop, which are included in the proceedings, were selected frompapers submitted by researchers worldwide. Due to time limitations at the workshop, not all of the submitted papers were selected for presentation. FOAL also welcomed an invited talk by James Riely (DePaul University), the abstract of which is included below. The workshop was organized by Gary T. Leavens (Iowa State University), Ralf L?ammel (CWI and Vrije Universiteit, Amsterdam), and Curtis Clifton (Iowa State University). The program committee was chaired by L?ammel and included L?ammel, Leavens, Clifton, Lodewijk Bergmans (University of Twente), John Tang Boyland (University of Wisconsin, Milwaukee), William R. Cook (University of Texas at Austin), Tzilla Elrad (Illinois Institute of Technology), Kathleen Fisher (AT&T Labs�Research), Radha Jagadeesan (DePaul University), Shmuel Katz (Technion�Israel Institute of Technology), Shriram Krishnamurthi (Brown University), Mira Mezini (Darmstadt University of Technology), Todd Millstein (University of California, Los Angeles), Benjamin C. Pierce (University of Pennsylvania), Henny Sipma (Stanford University), Mario S?udholt ( ?Ecole des Mines de Nantes), and David Walker (Princeton University). We thank the organizers of AOSD 2004 for hosting the workshop

Is the Stack Distance Between Test Case and Method Correlated With Test Effectiveness?

Mutation testing is a means to assess the effectiveness of a test suite and its outcome is considered more meaningful than code coverage metrics. However, despite several optimizations, mutation testing requires a significant computational effort and has not been widely adopted in industry. Therefore, we study in this paper whether test effectiveness can be approximated using a more light-weight approach. We hypothesize that a test case is more likely to detect faults in methods that are close to the test case on the call stack than in methods that the test case accesses indirectly through many other methods. Based on this hypothesis, we propose the minimal stack distance between test case and method as a new test measure, which expresses how close any test case comes to a given method, and study its correlation with test effectiveness. We conducted an empirical study with 21 open-source projects, which comprise in total 1.8 million LOC, and show that a correlation exists between stack distance and test effectiveness. The correlation reaches a strength up to 0.58. We further show that a classifier using the minimal stack distance along with additional easily computable measures can predict the mutation testing result of a method with 92.9% precision and 93.4% recall. Hence, such a classifier can be taken into consideration as a light-weight alternative to mutation testing or as a preceding, less costly step to that.Comment: EASE 201