In our previous work, we proposed wavelet shrinkage estimation (WSE) for nonhomogeneous Poisson process (NHPP)-based software reliability models (SRMs), where WSE is a data-transform-based nonparametric estimation method. Among many variance-stabilizing data transformations, the Anscombe transform and the Fisz transform were employed. We have shown that it could provide higher goodness-of-fit performance than the conventional maximum likelihood estimation (MLE) and the least squares estimation (LSE) in many cases, in spite of its non-parametric nature, through numerical experiments with real software-fault count data. With the aim of improving the estimation accuracy of WSE, in this paper we introduce other three data transformations to preprocess the software-fault count data and investigate the influence of different data transformations to the estimation accuracy of WSE through goodness-of-fit test.

Xiao Xiao

Tadashi Dohi

A Comparative Study of Data Transformations for Wavelet Shrinkage Estimation with Application to Software Reliability Assessment

We have proposed a detection method of fault-prone modules based on the spam filtering technique, &#x201c;Fault-prone filtering.&#x201d; Fault-prone filtering is a method which uses the text classifier (spam filter) to classify source code modules in software. In this study, we propose an extension to use warning messages of a static code analyzer instead of raw source code. Since such warnings include useful information to detect faults, it is expected to improve the accuracy of fault-prone module prediction. From the result of experiment, it is found that warning messages of a static code analyzer are a good source of fault-prone filtering as the original source code. Moreover, it is discovered that it is more effective than the conventional method (that is, without static code analyzer) to raise the coverage rate of actual faulty modules.

Osamu Mizuno

Michi Nakai

Can Faulty Modules Be Predicted by Warning Messages of Static Code Analyzer?

The size and complexity of industrial strength software systems are constantly increasing. This means that the task of managing a large software project is becoming even more challenging, especially in light of high turnover of experienced personnel. Software clustering approaches can help with the task of understanding large, complex software systems by automatically decomposing them into smaller, easier-to-manage subsystems. The main objective of this paper is to identify important research directions in the area of software clustering that require further attention in order to develop more effective and efficient clustering methodologies for software engineering. To that end, we first present the state of the art in software clustering research. We discuss the clustering methods that have received the most attention from the research community and outline their strengths and weaknesses. Our paper describes each phase of a clustering algorithm separately. We also present the most important approaches for evaluating the effectiveness of software clustering.

Mark Shtern

Vassilios Tzerpos

Clustering Methodologies for Software Engineering

A simple application program interface (API) for Java programs running on a wiki is implemented experimentally. A Java program with the API can be running on a wiki, and the Java program can save its data on the wiki. The Java program consists of PukiWiki, which is a popular wiki in Japan, and a plug-in, which starts up Java programs and classes of Java. A Java applet with default access privilege cannot save its data at a local host. We have constructed an API of applets for easy and unified data input and output at a remote host. We also combined the proposed API and the wiki system by introducing a wiki tag for starting Java applets. It is easy to introduce new types of applications using the proposed API. We have embedded programs such as a simple text editor, a simple music editor, a simple drawing program, and programming environments in a PukiWiki system using this API.

Takashi Yamanoue

Kentaro Oda

Koichi Shimozono

A Simple Application Program Interface for Saving Java Program Data on a Wiki

Any organization that develops software strives to improve the quality of its products. To do this first requires an understanding of the quality of the current product version. Then, by iteratively making changes, the software can be improved with subsequent versions. But this must be done in a systematic and methodical way, and, for this purpose, we have developed a specific strategy called SIQinU (Strategy for understanding and Improving Quality in Use). SIQinU recognizes problems of quality in use through evaluation of a real system-in-use situation and proposes product improvements by understanding and making changes to the product’s attributes. Then, reevaluating quality in use of the new version, improvement gains can be gauged along with the changes that led to those improvements. SIQinU aligns with GOCAME (Goal-Oriented Context-Aware Measurement and Evaluation), a multipurpose generic strategy previously developed for measurement and evaluation, which utilizes a conceptual framework (with ontological base), a process, and methods and tools. Since defining SIQinU relies on numerous phase and activity definitions, in this paper, we model different process views, for example, taking into account activities, interdependencies, artifacts, and roles, while illustrating them with excerpts from a real-case study.

Pablo Becker

Philip Lew

Luis Olsina

Specifying Process Views for a Measurement, Evaluation, and Improvement Strategy

This paper presents an approach to automatically analyzing program spectra, an execution profile of program testing results for fault localization. Using a mathematical theory of evidence for uncertainty reasoning, the proposed approach estimates the likelihood of faulty locations based on evidence from program spectra. Our approach is theoretically grounded and can be computed online. Therefore, we can predict fault locations immediately after each test execution is completed. We evaluate the approach by comparing its performance with the top three performing fault localizers using a benchmark set of real-world programs. The results show that our approach is at least as effective as others with an average effectiveness (the reduction of the amount of code examined to locate a fault) of 85.6&#37; over 119 versions of the programs. We also study the quantity and quality impacts of program spectra on our approach where the quality refers to the spectra support in identifying that a certain unit is faulty. The results show that the effectiveness of our approach slightly improves with a larger number of failed runs but not with a larger number of passed runs. Program spectra with support quality increases from 1&#37; to 100&#37; improves the approach&#39;s effectiveness by 3.29&#37;.

Rattikorn Hewett

Program Spectra Analysis with Theory of Evidence

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Advances in Software Engineering