Abstract
Developing software without failures is indeed important. Still, it is also important to detect as soon as possible when a running application is likely to fail, so that corrective actions can be taken. Following the guidelines of Agile Methods, the goal of our research is to develop a statistical prediction model for failures that does not require any additional effort on the side of the developers of an application; the key concept is that the developers concentrate on the code and we use the information that is naturally generated by the running application to assess whether an application is likely to fail. So the developers concentrate only on providing direct value to the customer and then the model takes care of informing the environment of the possible crash. The proposed model uses as input data that is commonly produced by developers: the log files. The statistical prediction model employed comes from biomedical studies about cancer survival prediction based on gene expression profiles where gene expression measurements and survival times of previous patients are used to predict future patients' survival. One of the most prominent models is the Cox Proportional Hazards (PH) model. In this work, we draw a parallel between our context and the biomedical one; we consider types of operations as genes, and operations and their multiplicity in the sequence as expression profiles. Then, we identify signature operations applying the above mentioned Cox PH model. We perform a prototypical analysis using real-world data to assess the suitability of our approach. We estimate the confidence interval of our results using Bootstrap.