Tomoyuki Yokogawa

Automatic verification of fault tolerance using model checking

Model checking is a technique that can make a verification for finite state systems absolutely automatic. We propose a method for automatic verification of fault-tolerant systems using this technique. Unlike other related work, which is tailored to specific systems, we are aimed at providing a general approach to verification of fault tolerance. The main obstacle in model checking is state explosion. To avoid the problem, we design this method so that it can use SMV, a symbolic model checking tool. Symbolic model checking can overcome the problem by expressing the state space and the transition relation by Boolean functions. Assuming that a system to be verified is specified by guarded commands, we define a modeling language suited for describing guarded command programs and propose a translation method from the modeling language to the input language of SMV. We show the results of applying the proposed method to various examples to demonstrate the usefulness.

Improving Relevancy Filter Methods for Cross-Project Defect Prediction

Context: Cross-project defect prediction (CPDP)research has been popular. One of the techniques for CPDP isa relevancy filter which utilizes clustering algorithms to selecta useful subset of the cross-project data. Their performanceheavily relies on the quality of clustering, and using an advancedclustering algorithm instead of simple ones used in the past studiescan contribute to the performance improvement. Objective:To propose and examine a new relevancy filter method usingan advanced clustering method DBSCAN (Density-Based SpatialClustering). Method: We conducted an experiment that examinedthe predictive performance of the proposed method. Theexperiments compared three relevancy filter methods, namely,Burak-filter, Peters-filter, and the proposed method with 56project data and four prediction models. Results: The predictiveperformance measures supported the proposed method. It wasbetter than Burak-filter and Peters-filter in terms of AUC andg-measure. Conclusion: The proposed method achieved betterprediction than the conventional methods. The results suggestedthat exploring advanced clustering algorithms could contributeto cross-project defect prediction.

Improving Cross-Project Defect Prediction Methods with Data Simplification

Context: Cross-project defect prediction (CPDP) research has been popular and many CPDP methods were proposed. While these methods used cross-project data as is for their inputs, useless or noisy information in the cross-project data can cause the degradation of predictive and computation performance. Removing such information makes the cross-project data simple and it will affect the performance of CPDP methods. Objective: To identify and quantify the effects of the data simplification for CPDP methods. Method: We conducted experiments that compared the predictive performance between CPDP with and without the data simplification. We adopted a data simplification method based on an active learning method proposed for software effort estimation. The experiments adopted 44 versions of OSS projects, four prediction models, and two CPDP methods, namely, Burak-filter and cross-project selection. Results: The data simplification achieved significant improvement in predictive performance for the cross-project selection. It did not improve Burak-filter. Conclusion: The data simplification can be helpful for the cross-project selection in terms of predictive performance and size reduction of cross-project data.

Performance Evaluation of Windowing Approach on Effort Estimation by Analogy

Background: In effort estimation model construction, it seems effective to window training project data so that only recently finished projects are used. This is because old projects might be less representative of an organization. The past study demonstrated windowing approach works with linear regression, which is one of global models. However, this approach has not been examined with local models. Local models use subset of historical data for model construction and thus windowing approach may influence on its performance more weakly. Aim: To investigate whether windowing approach works with local models. Method: We replicated the past study with EbA. Maxwell and CSC datasets were used for an experiment. Results: Windowing approach improved predictive performance. Although the difference was insignificant in any window size, the result indicated using windowing approach has positive effect on average. Conclusions: This result contributes to understand where windowing approach works well.

The Effects of Variable Selection Methods on Linear Regression-Based Effort Estimation Models

Stepwise regression has often been used for variable selection of effort estimation models. However it has been criticized for inappropriate selection, and another method is recommended. We thus examined the effects of Lasso, which is one of such variable selection methods. An experiment with datasets from PROMISE repository revealed that Lasso-based selection stably selected better variables than stepwise in predictive performance. We thus concluded Lasso-based selection is preferable to stepwise regression.

Empirical Analysis of Words in Comments Written for Java Methods

This paper focuses on comments written in source programs. While comments can work for improving the readability of code, i.e., the quality of programs, there have also been concerns thatcomments can be added for complicated source code in order to compensate for a lack of readability. That is to say, well-written comments might be associated with problematic parts to be refactored. This paper collected Java methods (programs) from six popular open source products, and performs analyses on words which appear in their comments. Then, the paper shows that a method having a longer comments (more words)tends to be more change-prone and would be required more fixes after their releases.

A Survival Analysis of Source Files Modified by New Developers

This paper proposes an application of the survival analysis to bug-fix events occurred in source files. When a source file is modified, it has a risk of creating a bug (fault). In this paper, such a risk is analyzed from a viewpoint of the survival time—the time that the source file can survive without any bug fix. Through an empirical study with 100 open source software (OSS) projects, the following findings are reported: (1) Source files modified by new developers have about \(26\%\) shorter survival time than the others. (2) The above tendency may be inverted if the OSS project has more developers relative to the total number of source files.

Visual Programming Language for Model Checkers Based on Google Blockly

Recently, model checkers, such as SPIN, have played an important role in the enhancement of software reliability. To promote the use of model checkers, we propose a visual programming language for SPIN model checkers for educational use. Our prototype is based on Google Blockly.

Tool Support for Consistency Verification of UML Diagrams

Manual verification of the consistency between UML state machine diagrams and sequence diagrams is labor-intensive and prone to make mistakes. We provide an automatic tool written in Java that performs the verification by translating UML diagrams into a process description of CSP\(_M\) language. The tool takes in a PlantUML file and verifies the consistency with a model-checker FDR.

A Replicated Study on Relationship Between Code Quality and Method Comments

Context: Recent studies empirically revealed a relationship between source code comments and code quality. Some studies showed well-written source code comments could be a sign of problematic methods. Other studies also show that source code files with comments confessing a technical debt (called self-admitted technical debt, SATD) could be fixed more times. The former studies only considered the amount of comments, and their findings might be due to a specific type of comments, namely, SATD comments used in the latter studies. Objective: To clarify the relationship between comments other than SATD comments and code quality. Method: Replicate a part of the latter studies with such comments of methods on four OSS projects. Results: At both the file-level and the method-level, the presence of comments could be related to more code fixings even if the comments were not SATD comments. However, SATD comments were more effective to spot fix-prone files and methods than the non-SATD comments. Conclusions: Source code comments other than SATD comments could still be a sign of problematic code. This study demonstrates a need for further analysis on the contents of comments and its relation to code quality.