Silvia Breu

Aspect mining using event traces

Aspect mining tries to identify crosscutting concerns in existing systems and thus supports the adaption to an aspect-oriented design. This work describes the first aspect mining approach that detects crosscutting concerns in legacy systems based on dynamic analysis. The analysis uses program traces that are generated in different program executions as underlying data pool. These traces are then investigated for recurring execution patterns based on different constraints, such as the requirement that the patterns have to exist in different calling contexts in the program trace. The implemented approach was evaluated in several case studies over systems with more than 80 kLoC. The tool was able to identify automatically both existing and seeded crosscutting concerns.

Improving bug tracking systems

It is important that information provided in bug reports is relevant and complete in order to help resolve bugs quickly. However, often such information trickles to developers after several iterations of communication between developers and reporters. Poorly designed bug tracking systems are partly to blame for this exchange of information being stretched over time. Our paper addresses the concerns of bug tracking systems by proposing four broad directions for enhancements. As a proof-of-concept, we also demonstrate a prototype interactive bug tracking system that gathers relevant information from the user and identifies files that need to be fixed to resolve the bug.

Inferring structural patterns for concern traceability in evolving software

As part of the evolution of software systems, effort is often invested to discover in what parts of the source code a feature (or other concern) is implemented. Unfortunately, knowledge about a concerns implementation can become invalid as the system evolves. We propose to mitigate this problem by automatically inferring structural patterns among the elements identified as relevant to a concerns implementation. We then document the inferred patterns as rules that can be checked as the source code evolves. Checking whether structural patterns hold across different versions of a system enables the automatic identification of new elements related to a documented concern. We implemented our technique for JAVA in an Eclipse plug-in called ISIS and applied it to a number of concerns. With a case study spanning 34 versions of the development history of an open-source system, we show how our approach supports the tracking of a concerns implementation through modifications such as extensions and refactorings

Mining eclipse for cross-cutting concerns

Software may contain functionality that does not align with its architecture. Such cross-cutting concerns do not exist from the beginning but emerge over time. By analysing where developers add code to a program, our history-based mining identifies cross-cutting concerns in a two-step process. First, we mine CVS archives for sets of methods where a call to a specific single method was added. In a second step, such simple cross-cutting concerns are combined to complex cross-cutting concerns. To compute these efficiently, we apply formal concept analysis---an algebraic theory. History-based mining scales well: we are the first to report aspects mined from an industrial-sized project like ECLIPSE. For example, we identified a locking concern that crosscuts 1284 methods.

Mining additions of method calls in ArgoUML

In this paper we refine the classical co-change to the addition of method calls. We use this concept to find usage patterns and to identify cross-cutting concerns for ArgoUML.

Aspect mining for large systems

The Eclipse plugin HAM identifies potential aspects in large programs. It analyzes the programs history and obtains sets of function calls that are likely to be cross-cutting. Later during programming, HAM informs the programmer when she is about to extend or change such a problematic concern.

HAM: cross-cutting concerns in Eclipse

As programs evolve, newly added functionality sometimes no longer aligns with the original design, ending up scattered across the software system. Aspect mining tries to identify such cross-cutting concerns in a program to support maintenance, or as a first step towards an aspect-oriented program. Previous approaches to aspect mining applied static or dynamic program analysis techniques to a single version of a system. We exploit all versions from a systems CVS history to mine aspect candidates; we are about to extend our research prototype to an Eclipse plug-in called HAM: when a single CVS commit adds calls to the same (small) set of methods in many unrelated locations, these method calls are likely to be cross-cutting. HAM employs formal concept analysis to identify aspect candidates. Analysing one commit operation at a time makes the approach scale to industrial-sized programs. In an evaluation we mined cross-cutting concerns from Eclipse 3.2M3 and found that up to 90% of the top-10 aspect candidates are truly cross-cutting concerns.