Soccer-lab

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Ongoing Projects

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  1. Grass GIS Software Evolution Project
  2. Term IDentifier RecognizER (TIDIER)
  3. arteFACT TRACEability (FACTRACE)
  4. Many-to-many Approximate Diagram Matching ( MADMatch)
  5. Software QUality ANalyzER ( SQUANER)
  6. AUtomatic change Rule Assistant (AURA )

GRASS

GRASS

A collaboration between the SOftware Cost-effective Change and Evolution Research (SOCCER) laboratory and the GRASS (Geographic Resources Analysis Support System) development team. The SOCCER laboratory is a research facility focused on software evolution located in the Département de Génie Informatique, École Polytechnique de Montréal. GRASS is the world leading free software GIS. Collaboration focuses on modeling software evolution and quality improvement via a planned set of refactoring actions.

Features

  • GRASS source code browser - Navigate dirs, files and functions GRASS statistic browser
  • Navigate developer contributions, CVS info, commit, code size GRASS clone browser
  • Navigate clone clusters, view clone deltas GRASS monster browser
  • Navigate monster functions GRASS comparandum tool
  • Compare files in an intelligent way GRASS Work Package Browser
  • Select a Work Package to Improve GRASS quality

 

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TIDIER

When high-level documentation is scarce or outdated and when source code is not sufficiently commented, the developers' main source of information is source code identifiers. To help disambiguate conceptual information encoded in compound (or abbreviated) identifiers, we suggested a novel contextual approach to split program identifiers. This approach is called TIDIER: Term IDentifier RecognizER. TIDIER uses high-level and domain concepts captured into multiple dictionaries to map identifiers to their corresponding domain concepts. More precisely, TIDIER uses contextual information in the form of specialized dictionaries (e.g., acronyms, contractions and domain specific terms) and mimics the process of transforming words via a set of contraction rules. TIDIER overcomes the limitations of existing identifier splitting approaches (e.g., Camel Case) when naming conventions are not used or when identifiers contain abbreviations. In addition, it outperforms alternative techniques when using a dictionary augmented with domain knowledge or a contextual dictionary. More details can be found in. Since identifier splitting is one of the essential ingredients in any feature location or traceability recovery technique, we are currently investigating the use of enhanced splitting techniques in the context of feature location and traceability.

 

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FACTRACE

FacTrace, for arteFACT TRACEability, provides several modules that help from traceability recovery to traceability links verification. it aids software engineers in different tasks, namely, requirement elicitation, requirement analysis, artefact traceability, and most importantly for Trust-based traceability. FacTrace has a graphical interface to perform different tasks. It is a research tool, if you need access to its control panel then please contact us. You can view the publication of FacTrace.

Features

  • Recover, manage, and perform requirement similarity and variability analysis.
  • Divide recovered links among different software teams to speed up work, with the help of easy-to-use Web interface.
  • Improve productivity by tracking temporal information, such as CSV/SVN, bug reports, mailing lists so on, to automatically update traces.
  • Analyse the software changes impact on software quality.

 

 

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MADMatch

MADMatch is a generic tool for the matching of (software) diagrams. It can be used for the differencing of class diagrams, labelled transition systems, sequence diagrams and has been demonstrated to be more accurate, scalable and faster than previous state-of-art algorithms such as UMLDiff, AURA, and PLTSDiff. For MADMatch, matching (or differencing) tasks involving diagrams are formulated as Error Tolerant Graph Matching (ETGM) problems in which differences between the diagrams are modeled as edit operations. The resulting optimization problem (find the cheapest edition between the two diagrams) is subsequently solved using a tabu search enhanced by similarity concepts mixing lexical and structural information.

 

SQUANER

SQUANER, Software QUality ANalyzER, is a framework for monitoring the quality of object-oriented systems. SQUANER implements three quality models, 60 metrics, and design patterns, antipatterns, and code smells detection algorithms. Each time a contribution is made on the project, SQUANER detects the new changes automatically, downloads the new code and performs design patterns, antipatterns and code smells detections, quality evaluations, and faults predictions. After these analysis, a feedback is provided to the developer and the management team about the current quality of the system and instructions on how to improve this quality.

 

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AURA+

AURA+ is a tool to identify framework evolution. It combines call-dependency and text similarity analyses in a multi-iteration algorithm to detect change rules from old versions to new versions of a program. It is able to automatically generate one-to-one, many-to-one, one-to-many and simply-deleted rules. AURA+ includes two components:
AURA Model Builder: It converts the source code of the old version and the new version of a program to a language-independent model (AURA Model), used by AURA Rule Generator to identify the change rules. The current version of AURA Model Builder is an Eclipse plugin.
AURA Rule Generator: It generates change rules according to AURA Model. It can be used both as an Eclipse plugin and as a standalone java program.