A Framework to Compare Alert Ranking Algorithms

Allier, Simon; Hora, Andre; Anquetil, Nicolas; Ducasse, Stephane

Type de document :

Communication dans un congrès avec actes

Titre :

A Framework to Compare Alert Ranking Algorithms

Auteur(s) :

Allier, Simon [Auteur correspondant]
Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Hora, Andre [Auteur]
Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Anquetil, Nicolas [Auteur]

Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Ducasse, Stephane [Auteur]

Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]

Titre de la manifestation scientifique :

19th Working Conference on Reverse Engineering

Ville :

Kingston

Pays :

Canada

Date de début de la manifestation scientifique :

2012-10-15

Titre de l’ouvrage :

19th Working Conference on Reverse Engineering (WCRE 2012)

Date de publication :

2012-10-15

Discipline(s) HAL :

Informatique [cs]/Génie logiciel [cs.SE]

Résumé en anglais : [en]

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 ...
Lire la suite >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. Unfor- tunately, 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". In this paper, 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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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A Framework to Compare Alert Ranking Algorithms

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