Section: New Results

Warnings and bugs

Participants : Simon Allier [Correspondant] , Andre Hora, Nicolas Anquetil, Muhammad Bhatti, Stéphane Ducasse.

A Framework to Compare Alert Ranking Algorithms To improve software quality, rule checkers statically check if a software contains violations of good programming practices. On a real sized system, the alerts (rule violations detected by the tool) may be numbered by the thousands. Unfortunately, these tools generate a high proportion of "false alerts", which in the context of a specific software, should not be fixed. Huge numbers of false alerts may render impossible the finding and correction of "true alerts" and dissuade developers from using these tools. In order to overcome this problem, the literature provides different ranking methods that aim at computing the probability of an alert being a "true one". We propose a framework for comparing these ranking algorithms and identify the best approach to rank alerts. We have selected six algorithms described in literature. For comparison, we use a benchmark covering two programming languages (Java and Smalltalk) and three rule checkers (FindBug, PMD, SmallLint). Results show that the best ranking methods are based on the history of past alerts and their location. We could not identify any significant advantage in using statistical tools such as linear regression or Bayesian networks or ad-hoc methods [15] .

Uncovering Causal Relationships between Software Metrics and Bugs Bug prediction is an important challenge for software engineering research that consists in looking for possible early indicators of the presence of bugs in a software. However, despite the relevance of the issue, most experiments designed to evaluate bug prediction only investigate whether there is a linear relation between the predictor and the presence of bugs. However, it is well known that standard regression models can not filter out spurious relations. We describe an experiment to discover more robust evidences towards causality between software metrics (as predictors) and the occurrence of bugs. For this purpose, we have relied on Granger Causality Test to evaluate whether past changes in a given time series are useful to forecast changes in another series. As its name suggests, Granger Test is a better indication of causality between two variables. We present and discuss the results of experiments on four real world systems evaluated over a time frame of almost four years. Particularly, we have been able to discover in the history of metrics the causes - in the terms of the Granger Test - for 64% to 93% of the defects reported for the systems considered in our experiment [18] .

BugMaps: A Tool for the Visual Exploration and Analysis of Bugs To harness the complexity of big legacy software, software engineering tools need more and more information on these systems. This information may come from analysis of the source code, study of execution traces, computing of metrics, etc. One source of information received less attention than source code: the bugs on the system. Little is known about the evolutionary behavior, lifetime, distribution, and stability of bugs. We propose to consider bugs as first class entities and a useful source of information that can answer such topics. Such analysis is inherently complex, because bugs are intangible, invisible, and difficult to be traced. Therefore, our tool extracts information about bugs from bug tracking systems, link this information to other software artifacts, and explore interactive visualizations of bugs that we call bug maps [19] .

A Catalog of Patterns for Concept Lattice Interpretation in Software Reengineering Formal Concept Analysis (FCA) provides an important approach in software reengineering for software understanding, design anomalies detection and correction. However, FCA-based approaches have two problems: (i) they produce lattices that must be interpreted by the user according to his/her understanding of the technique and different elements of the graph; and, (ii) the lattice can rapidly become so big that one is overwhelmed by the mass of information and possibilities. We make a catalog of important patterns in concept lattices, which can allow automating the task of lattice interpretation. The approach helps the reengineer to concentrate on the task of reengineering rather than understanding a complex lattice. We provide interpretation of these patterns in a generalized manner and illustrate them on various contexts constructed from program information of different open-source systems. We also present a tool that allows automated extraction of the patterns from concept lattices [16] .