Searching for rules to detect defective modules: A subgroup discovery approach

By Daniel Rodríguez, Roberto Ruiz Sánchez, José Cristóbal Riquelme Santos, J. Aguilar–Ruiz. 2012

63 citations recorded by the source corpus.

Data mining methods in software engineering are becoming increasingly important as they can support several aspects of the software development life-cycle such as quality. In this work, we present a data mining approach to induce rules extracted from static software metrics characterising fault-prone modules. Due to the special characteristics of the defect prediction data (imbalanced, inconsistency, redundancy) not all classification algorithms are capable of dealing with this task conveniently. To deal with these problems, Subgroup Discovery (SD) algorithms can be used to find groups of statistically different data given a property of interest. We propose EDER-SD (Evolutionary Decision Rules for Subgroup Discovery), a SD algorithm based on evolutionary computation that induces rules describing only fault-prone modules. The rules are a well-known model representation that can be easily understood and applied by project managers and quality engineers. Thus, rules can help them to develop software systems that can be justifiably trusted. Contrary to other approaches in SD, our algorithm has the advantage of working with continuous variables as the conditions of the rules are defined using intervals. We describe the rules obtained by applying our algorithm to seven publicly available datasets from the PROMISE repository showing that they are capable of characterising subgroups of fault-prone modules. We also compare our results with three other well known SD algorithms and the EDER-SD algorithm performs well in most cases.

Subfields: Information Systems, Software

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Himabindu Lakkaraju
2. Jure Leskovec
3. Ruchika Malhotra
4. Baowen Xu
5. Qinbao Song
6. Stephen H. Bach
7. Yuming Zhou
8. Tong Wang

In Software

1. Ruchika Malhotra
2. Baowen Xu
3. Qinbao Song
4. Jure Leskovec
5. Yuming Zhou
6. Cagatay Catal
7. Ahmed E. Hassan

Citations in the corpus

• Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “Similarly, algorithms for problems such as subgroup discovery [24, 33, 40, 46], contrast set learning [3, 4, 30], and emerging pattern mining [17, 19] identify sets of rules to describe the relationships among variables and discover interesting patterns in the data.”
• A systematic review of machine learning techniques for software fault prediction (2015, background): “Hence, total number of 122 studies [1, 2, 20-139] were identified for further processing and analysis.”
• A novel ensemble method for classifying imbalanced data (2015, methodology): “Internal methods modify existing classification algorithms for reducing their sensitiveness to class imbalance [41, 52, 53, 54], while external methods preprocess the training data to make them balanced.”