Jelber Sayyad Shirabad

Supporting software maintenance by mining software update records

This paper describes the application of inductive methods to data extracted from both source code and software maintenance records. We would like to extract relations that indicate which files in, a legacy system, are relevant to each other in the context of program maintenance. We call these relations maintenance relevance relations. Such a relation could reveal existing complex interconnections among files in the system, which may in turn be useful in comprehending them. We discuss the methodology we employed to extract and evaluate the relations. We also point out some of the problems we encountered and our solutions for them. Finally, we present some of the results that we have obtained.

Mining the maintenance history of a legacy software system

A considerable amount of system maintenance experience can be found in bug tracking and source code configuration management systems. Data mining and machine learning techniques allow one to extract models from past experience that can be used in future predictions. By mining the software change record, one can therefore generate models that can be used in future maintenance activities. In this paper, we present an example of such a model that represents a relation between pairs of files and show how it can be extracted from the software update records of a real world legacy system. We show how different sources of data can be used to extract sets of features useful in describing this model, as well as how results are affected by these different feature sets and their combinations. Our best results were obtained from text-based features, i.e. those extracted from words in the problem reports as opposed to syntactic structures in the source code.

Implementing an Integrative Multi-agent Clinical Decision Support System with Open Source Software

Clinical decision making is a complex multi-stage process. Decision support can play an important role at each stage of this process. At present, the majority of clinical decision support systems have been focused on supporting only certain stages. In this paper we present the design and implementation of MET3—a prototype multi-agent system providing an integrative decision support that spans over the entire decision making process. The system helps physicians with data collection, diagnosis formulation, treatment planning and finding supporting evidence. MET3 integrates with external hospital information systems via HL7 messages and runs on various computing platforms available at the point of care (e.g., tablet computers, mobile phones). Building MET3 required sophisticated and reliable software technologies. In the past decade the open source software movement has produced mature, stable, industrial strength software systems with a large user base. Therefore, one of the decisions that should be considered before developing or acquiring a decision support system is whether or not one could use open source technologies instead of proprietary ones. We believe MET3 shows that the answer to this question is positive.

Improving co-training with agreement-based sampling

Co-training is an effective semi-supervised learning method which uses unlabeled instances to improve prediction accuracy. In the cotraining process, a random sampling is used to gradually select unlabeled instances to train classifiers. In this paper we explore whether other sampling methods can improve co-training performance. A novel selective sampling method, agreement-based sampling, is proposed. Experimental results show that our new sampling method can improve co-training significantly.

Predictor models in software engineering (PROMISE)

No abstract available

Predictive software models

A predictive software model (PSM) is any model extracted from software engineering data that can be readily used to make a prediction regarding some aspect of a software system. In this paper, we present some well known applications of predictive software models, and propose new potential applications for PSMs. We also introduce the promise software engineering repository of public datasets. The purpose of this repository is to promote repeatable, verifiable and refutable research in the area of predictive software models. We conclude the paper with our observations about the software engineering datasets used in building PSMs

Active Learning with Automatic Soft Labeling for Induction of Decision Trees

Decision trees have been widely used in many data mining applications due to their interpretable representation. However, learning an accurate decision tree model often requires a large amount of labeled training data. Labeling data is costly and time consuming. In this paper, we study learning decision trees with lesser labeling cost from two perspectives: data quality and data quantity. At each step of active learning process we learn a random forest and then use it to label a large quantity of unlabeled data. To overcome the large tree size caused by the machine labeling, we generate weighted (soft) labeled data using the prediction confidence of the labeling classifier. Empirical studies show that our method can significantly improve active learning in terms of labeling cost for decision tree learning, and the improvement does not sacrifice the size of decision trees.