物件導向軟體之架構(Object-Oriented Software Construction)探討

Patch generation is an essential software maintenance task because most software systems inevitably have bugs that need to be fixed. Unfortunately, human resources are often insufficient to fix all reported and known bugs. To address this issue, several automated patch generation techniques have been proposed. In particular, a genetic-programming-based patch generation technique, GenProg, proposed by Weimer et al., has shown promising results. However, these techniques can generate nonsensical patches due to the randomness of their mutation operations. To address this limitation, we propose a novel patch generation approach, Pattern-based Automatic program Repair (Par), using fix patterns learned from existing human-written patches. We manually inspected more than 60,000 human-written patches and found there are several common fix patterns. Our approach leverages these fix patterns to generate program patches automatically. We experimentally evaluated Par on 119 real bugs. In addition, a user study involving 89 students and 164 developers confirmed that patches generated by our approach are more acceptable than those generated by GenProg. Par successfully generated patches for 27 out of 119 bugs, while GenProg was successful for only 16 bugs.

/pdf/automatic-patch-generation-learned-from-human-written-33lkn2hgje.pdf

Automatic patch generation learned from human-written patches

Object-Oriented (OO) code stereotypes are low-level patterns that reveal the design intent of a source code artifact, such as, a method or a class. They are orthogonal to the problem domain of the software and they reflect the role of a method or class from the OO problem solving point of view. However, the research community in automated reverse engineering has focused more on higher-level design information, such as design patterns. Existing work on reverse engineering code stereotypes is scarce and focused on C++ code, while no tools are freely available as of today. We present JStereoCode, a tool that automatically identifies the stereotypes of methods and classes in Java systems. The tool is integrated with Eclipse and for a given Java project will classify each method and class in the system based on their stereotypes. Applications of JStereoCode include: program comprehension, defect prediction, etc.

JStereoCode: automatically identifying method and class stereotypes in Java code

A tool chain for reverse engineering C++ applications

One of the characteristics of the increasingly widespread use of object-oriented libraries and the resulting intensive use of inheritance is the proliferation of dependencies on abstract classes. Since abstract classes cannot be instantiated, they cannot be tested in isolation using standard execution-based testing strategies. A standard approach to testing abstract classes is to instantiate a concrete descendant class and test the features that are inherited. This paper presents a structured approach that supports the testing of features in abstract classes, paying particular attention to ensuring that the features tested are those defined in the abstract class. Two empirical studies are performed on a suite of large Java programs and the results presented. The first study analyses the role of abstract classes from a testing perspective. The second study investigates the impact of the testing strategy on the programs in this suite to demonstrate its feasibility and to comment on the pragmatics of its use. Copyright © 2010 John Wiley & Sons, Ltd.

https://www.cs.nuim.ie/%7Ejpower/Research/Papers/2012/clarke-stvr12.pdf

A testing strategy for abstract classes

Analyzing clusters of class characteristics in OO applications

In this paper, we propose an innovative suite of metrics based on a class abstraction that uses a taxonomy for OO classes (CAT) to capture aspects of software complexity through combinations of class characteristics. We empirically validate their ability to predict fault prone classes using fault data for six versions of the Java-based open-source Eclipse Integrated Development Environment. We conclude that this proposed CAT metric suite, even though it treats classes in groups rather than individually, is as effective as the traditional Chidamber and Kemerer metrics in identifying fault-prone classes.

Using a class abstraction technique to predict faults in OO classes: a case study through six releases of the Eclipse JDT

One of the characteristics of the increasingly widespread use of object-oriented libraries and the resulting intensive use of inheritance is the proliferation of dependencies on abstract classes. Such classes defer the implementation of some features, and are typically used as a specification or design tool. However, since their features are not fully implemented, abstract classes cannot be instantiated, and thus pose challenges for execution-based testing strategies. This paper presents a structured approach that supports the testing of features in abstract classes. Core to the approach is a series of static analysis steps that build a comprehensive view of the inter-class dependencies in the system under test. We then leveraged this information to define a test order for the methods in an abstract class that minimizes the number of stubs required during testing, and clearly identifies the required functionality of these stubs. Our approach is based on a comprehensive taxonomy of object-oriented classes that provides a framework for our analysis. First we describe the algorithms to calculate the inter-class dependencies and the test-order that minimizes stub creation. Then we give an overview of our tool, AbstractTestJ that implements our approach by generating a test order for the methods in an abstract Java class. Finally, we harness this tool to provide an analysis of 12 substantial Java applications that demonstrates both the feasibility of our approach and the importance of this technique.

/pdf/intra-class-testing-of-abstract-class-features-4nbbz29qmj.pdf

Intra-Class Testing of Abstract Class Features

The object-oriented (OO) paradigm provides several benefits during analysis and design of large-scale software systems, but scores lower in terms of testability. The low testability score for OO software is due mainly to the composition of OO systems exhibiting the characteristics of abstraction, encapsulation, genericity, inheritance, polymorphism, concurrency and exception handling. To address the difficulty of testing the features of a class, a plethora of implementation-based testing techniques (IBTTs) have been developed. However, no one IBTT has emerged as the preferred technique to test the implementation of a class. In this paper we present a technique that automatically identify those IBTTs that are most suitable for testing a class based on the characteristics of that class. Our approach uses a taxonomy of OO classes that is used to succinctly abstract the characteristics of a class under test (CUT). We have implemented a tool that automates the process of mapping IBTTs to a class. In addition to identifying the IBTTs that would be best suited for testing a class, our tool provides feedback to the tester facilitating the identification of the characteristics of the class that are not suitably tested by any of the IBTTs in the list. We provide results of a study supporting the notion that using more than on IBTT during testing improves test coverage of a CUT.

A tool to automatically map implementation-based testing techniques to classes

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