Do System Test Cases Grow Old?

Companies increasingly use either manual or automated system testing to ensure the quality of their software products. As a system evolves and is extended with new features the test suite also typically grows as new test cases are added. To ensure software quality throughout this process the test suite is continously executed, often on a daily basis. It seems likely that newly added tests would be more likely to fail than older tests but this has not been investigated in any detail on large-scale, industrial software systems. Also it is not clear which methods should be used to conduct such an analysis. This paper proposes three main concepts that can be used to investigate aging effects in the use and failure behavior of system test cases: test case activation curves, test case hazard curves, and test case half-life. To evaluate these concepts and the type of analysis they enable we apply them on an industrial software system containing more than one million lines of code. The data sets comes from a total of 1,620 system test cases executed a total of more than half a million times over a time period of two and a half years. For the investigated system we find that system test cases stay active as they age but really do grow old; they go through an infant mortality phase with higher failure rates which then decline over time. The test case half-life is between 5 to 12 months for the two studied data sets.Comment: Updated with nicer figs without border around the

Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.Comment: 10 page

PhysicsGP: A Genetic Programming Approach to Event Selection

We present a novel multivariate classification technique based on Genetic Programming. The technique is distinct from Genetic Algorithms and offers several advantages compared to Neural Networks and Support Vector Machines. The technique optimizes a set of human-readable classifiers with respect to some user-defined performance measure. We calculate the Vapnik-Chervonenkis dimension of this class of learning machines and consider a practical example: the search for the Standard Model Higgs Boson at the LHC. The resulting classifier is very fast to evaluate, human-readable, and easily portable. The software may be downloaded at: http://cern.ch/~cranmer/PhysicsGP.htmlComment: 16 pages 9 figures, 1 table. Submitted to Comput. Phys. Commu