Abstract—We describe a novel application of using data mining and statistical learning methods to automatically monitor and detect abnormal execution traces from console logs in an online setting. Different from existing solutions, we use a two stage detection system. The first stage uses frequent pattern mining and distribution estimation techniques to capture the dominant patterns (both frequent sequences and time duration). The second stage use principal component analysis based anomaly detection technique to identify actual problems. Using real system data from a 203-node Hadoop [1] cluster, we show that we can not only achieve highly accurate and fast problem detection, but also help operators better understand execution patterns in their system. I. MOTIVATION AND OVERVIEW Internet services today often run in data centers consisting

Our work is concerned with an enriched perspective of what constitutes developer contribution in software infrastructures supporting incremental development and distributed software projects. We use the term “contribution” to express the combination of all the actions a developer has performed during the development process and propose a model for calculating this individually for developers participating in a software project. Our approach departs from the traditional practice of only measuring the contribution to the final outcome (the code) and puts emphasis additionally on other activities that do not directly affect the product itself but are essential to the development process.We use the Open Source Software (OSS) context to take advantage of the public availability of data in software repositories. In this paper, we present our method of calculation and its system implementation and we apply our measurements on various projects from the gnome ecosystem.

Abstract:- In this work we propose an algorithm for computing the fractal dimension of a software network, and compare its performances with two other algorithms. Object of our study are various large, object-oriented software systems. We built the associated graph for each system, also known as software network, analyzing the binary relationships (dependencies), among classes. We found that the structure of such software networks is self-similar under a length-scale transormation, confirming previous results of a recent paper from the authors. The fractal dimension of these networks is computed using a Merge algorithm, first devised by the authors, a Greedy Coloring algorithm, based on the equivalence with the graph coloring problem, and a Simulated Annealing algorithm, largely used for efficiently determining minima in multi-dimensional problems. Our study examines both efficiency and accuracy, showing that the Merge algorithm is the most efficient, while the Simulated Annealing is the most accurate. The Greeding Coloring algorithm lays in between the two, having speed very close to the Merge algorithm, and accuracy comparable to the Simulated Annealing algorithm. Key-Words:-