Currently, many competitive programming contests have been held. These contests are composed of several problems of a wide range of difficulty levels. Novice programmers must approach problems based on their experience and levels to avoid losing confidence and motivation to learn programming skills. In some programming contests, the difficulty level of a problem is given as a numerical value. However, each contest site assigns difficulty levels based on its criteria, making comparing levels among two or more contest sites impossible. This study proposes a method to estimate the difficulty level from the problem description and example solution information. Specifically, we attempted to estimate the difficulty of the problems using GPT-3.5 Turbo, which was fine-tuned with the problem descriptions and example solutions. The experiment was conducted on the fine-tuned GPT-3.5 Turbo under five conditions to evaluate the performance of difficulty estimation. As a result, a model that was fine-tuned with problem descriptions and estimated difficulty from the problem description had the best performance. In addition, the performance of the model fine-tuned with problem descriptions only was better than that of the model fine-tuned with problem descriptions and example solutions.

Regression testing is required to check the changes in behavior whenever developers make any changes to a software system. The cost of regression testing is a major problem because developers have to frequently update dependent components to minimize security risks and potential bugs. In this paper, we report a current practice in a company that maintains an industrial simulator as a critical component of their business. The simulator automatically records all the users’ requests and the simulation results in storage. The feature provides a huge number of test cases for regression testing to developers; however, their time budget for testing is limited (i.e., at most one night). Hence, the developers need to select a small number of test cases to confirm both the simulation result and execution performance are unaffected by an update of a dependent component. In other words, the test cases should achieve high coverage while keeping diversity of execution time. To solve the problem, we have developed a clustering-based method to select test cases, using the similarity of execution traces produced by them. The developers have used the method for a half year; they recognize that the method is better than the previous rule-based method used in the company.

In Java programs, access modifiers are used to control the accessibility of fields and methods from other objects. Choosing appropriate access modifiers is one of the key factors to improve program quality and to reduce potential vulnerability. In our previous work, we presented a static analysis method named Accessibility Excessiveness (AE) detection for each field and method in Java program. We have also developed an AE analysis tool named ModiChecker that analyzes each field and method of the input Java programs, and reports their excessiveness. In this paper, we have applied ModiChecker to several OSS repositories to investigate the evolution of AE over versions, and identified transition of AE status and the difference in the amount of AE change between major version releases and minor ones. Also we propose when to evaluate source code with AE analysis.

The increasing performance-price ratio of computer hardware makes possible to explore a distributed approach at code clone analysis. This paper presents D-CCFinder, a distributed approach at large-scale code clone analysis. D-CCFinder has been implemented with 80 PC workstations in our student laboratory, and a vast collection of open source software with about 400 million lines in total has been analyzed with it in about 2 days. The result has been visualized as a scatter plot, which showed the presence of frequently used code as easy recognizable patterns. Also, D-CCFinder has been used to analyze a single software system against the whole collection in order to explore the presence of code imported from open source software.

This paper briefly describes the theme and goals of the WebSDE Workshop on ASE'2006. This workshop emphasizes next-generation software development environments inspired by Web2.0 technologies and seeks to explore ways of automated support to software development in the Web2.0 era.

In typical open source software development, developers use revision control systems for product management, mailing list systems for human communications, and bug tracking systems for process management. All of these systems store development histories of the products that show significant information of problems during the development. However, it would be a hard job to retrieve useful information related to a current problem faced by developers. In this paper, we describe a software development supporting system CoxR that is capable of crawling the development histories. CoxR creates software development information Web which consists of developers, emails, and program deltas, and provides an interface to search, navigate, browse, and retrieve past development results. Through a case study, we confirmed that CoxR helps developers to solve their problems by making it easier to search development history.

Collections of already developed programs are important resources for efficient development of reliable software systems. In this paper, we propose a novel graph-representation model of a software component library (repository), called component rank model. This is based on analyzing actual usage relations of the components and propagating the significance through the usage relations. Using the component rank model, we have developed a Java class retrieval system named SPARS-J and applied SPARS-J to various collections of Java files. The result shows that SPARS-J gives a higher rank to components that are used more frequently. As a result, software engineers looking for a component have a better chance of finding it quickly. SPARS-J has been used by two companies, and has produced promising results.

Open source communities typically use a software repository to archive various software projects with their source code, mailing list discussions, documentation, bug reports, and so forth. For example, SourceForge currently hosts over seventy thousand open source software systems. Because of the size of the rich information content, such repositories offer numerous opportunities for sharing information among projects. For example, one would like to know a set of projects that are related or similar to each other, so that the project groups can collaborate and share their work. With thousands of projects in typical repositories, however, manually locating related projects can be difficult. Hence, we propose MUDABlue, a tool that automatically categorizes software systems. MUDABlue has three major aspects: 1) it relies on no other information than the source code, 2) it determines category sets automatically, and 3) it allows a software system to be a member of multiple categories. MUDABlue has a Web interface to visualize determined categories, which eases browsing a software repository. We show the effectiveness of MUDABlue's categorization capability by comparing its generated categories with that of some other existing research tools.

Collections of already developed programs are important resources for efficient development of reliable software systems. In this paper, we propose a novel method of ranking software components, called Component Rank, based on analyzing actual use relations among the components and propagating the significance through the use relations. We have developed a component-rank computation system, and applied it to various Java programs. The result is promising such that non-specific and generic components are ranked high. Using the Component Rank system as a core part, we are currently developing Software Product Archiving, analyzing, and Retrieving System named SPARS.

In our research demonstration, we show two development support systems for Java programs. One is an Aspect-oriented Dynamic Analysis and Slice calculation system named ADAS, and another is a Software Product archiving, Analyzing, and Retrieving System for Java named SPARS-J.

It is essential to manage versions of software products created during software development. There are various versioning tools actually used in these days, although most of them require the developers to issue management commands for consistent versioning. In this paper, we present a novel versioning file system Moraine, which accumulatively and automatically collects all files created or modified. Those files are versioned and stored as compressed forms. The older versions are easily retrieved from Moraine by the time-stamps or tags if required.

We propose an object-centered software process description model. We also present the idea of a software development management environment based on the model. To use this model and environment, we illustrate the software development environment as it is, and provide a framework for software process description, management and improvement.

The paper proposes a new modeling method of interactions in the software development process, which focuses on the interactions among the elements of the process, and a new software development environment based on the model. In this method, interactions in the software process are modeled as a set of agents and communication channels. An agent interacts with other agents with channels. Channels are classified according to their content and type of interaction. A prototype of the supporting environment for software development which is based on the model is also developed. The environment consists of a proxy program for the agent and integrated communication server, which provides mechanisms for interaction, process execution, and user navigation.