@conference {455,
	title = {Software Fault Prediction : A Systematic Mapping Study},
	booktitle = {Ibero-American Conference  on Software Engineering  },
	year = {2015},
	address = {Lima, Peru},
	abstract = {Context: Software fault prediction has been an important research topic in the software engineering field for more than 30 years.
Software defect prediction models are commonly used to detect faulty software modules based on software metrics collected during the software development process. Objective: Data mining techniques and machine learning studies in the fault prediction software context are mapped and characterized. We investigated the metrics and techniques and their performance according to performance metrics studied. An analysis and synthesis of these studies is conducted. Method: Asystematicmapping study has been conducted for identifying and aggregating evidence about software fault prediction. Results: About 70 studies published from January 2002 to December 2014 were identified. Top 40 studies were selected for analysis, based on the quality criteria results. The main metrics used were: Halstead, McCabe and LOC (67.14\%), Halstead, McCabe and LOC + Object-Oriented (15.71\%), others (17.14\%). The main models were: Machine Learning(ML) (47.14\%), ML + Statistical Analysis (31.42\%), others (21.41\%). The data sets used were: private access (35\%) and public access (65\%). The most frequent combination of metrics, models and techniques were: Halstead, McCabe and LOC + Random Forest, Naive Bayes, Logistic Regression and Decision Tree representing the (60\%) of the analyzed studies. Conclusions: This article has identified and classified the performance of the metrics, techniques and their combinations. This will help researchers to select datasets, metrics and models based on experimental results, with the objective to generate learning schemes that allow a better prediction software failures.},
	keywords = {fault prediction models, software metrics, software quality},
	isbn = {978-9972-825-80-4},
	url = {http://eventos.spc.org.pe/cibse2015/pdfs/15_ESELAW15.pdf},
	author = {Murillo-morera, Juan and Quesada-L{\'o}pez, Christian and Jenkins, Marcelo}
}
@conference {406,
	title = {Application of Statistical Process Control to Software Defect Metrics: An Industry Experience Report},
	booktitle = {Empirical Software Engineering and Measurement, 2013 ACM / IEEE International Symposium on},
	year = {2013},
	month = {08/2013},
	abstract = {<p>Statistical Process Control (SPC) has become of great significance for software engineering organizations as more of them decide to implement quality improvement initiatives. The Capability Maturity Model Integration (CMMI-DEV 1.3) for example, proposes the use of statistical techniques at maturity level 4 to ensure some degree of process predictability. However, the nature of software products and processes poses many challenges to the application of SPC, mainly regarding the design of control charts, a key tool. These challenges have led to opposing views on the applicability of SPC to software processes. This article presents an industry experience report on the application of SPC in a Software Verification and Validation Unit at an Information Technology Division from a Financial institution. We present the steps followed to implement SPC in this organization, describe the theoretical assumptions involved in selecting the appropriate control charts, and show a process improvement analysis of using SPC in the organization.</p>},
	keywords = {quantitative management, software metrics, software process improvement, statistical process control},
	isbn = {978-0-7695-5056-5},
	doi = {10.1109/ESEM.2013.51},
	author = {Fernandez-Corrales, C. and Jenkins, M. and Villegas, J.}
}
