Visualizing an Execution Trace as a Compact Sequence Diagram Using Dominance Algorithms

Visualizing an execution trace of an object-oriented system as sequence diagrams is effective to understand the behavior of the system. However, sequence diagrams extracted from an execution trace are too large for developers to inspect since a trace involves a large number of objects and method calls. To support developers to understand extracted sequence diagrams, it is necessary to remove the less important details of the diagrams. In this paper, we apply a dominance algorithm to a dynamic call graph among objects in order to detect and remove local objects contributing to internal behavior of dominator objects. The case study shows our approach automatically removed about 40 percent of the objects from execution traces on average.4th International Workshop on Program Comprehension through Dynamic Analysis(PCODA'08)co-located with the 15th International Working Conference on Reverse Engineering (WCRE’08)October 16th, 2008 – Antwerp, BelgiumAndy Zaidman, Abdelwahab Hamou-Lhadj, Orla Greevy, David Röthlisberger(editors)刊行年月日は会議開催日を参考にし

Supporting feature-level software maintenance

Software maintenance is the process of modifying a software system to fix defects, improve performance, add new functionality, or adapt the system to a new environment. A maintenance task is often initiated by a bug report or a request for new functionality. Bug reports typically describe problems with incorrect behaviors or functionalities. These behaviors or functionalities are known as features. Even in very well-designed systems, the source code that implements features is often not completely modularized. The delocalized nature of features makes maintaining them challenging. Since maintenance tasks are expressed in terms of features, the goal of this dissertation is to support software maintenance at the feature-level. We focus on two tasks in particular: feature location and impact analysis via feature coupling.;Feature location is the process of identifying the source code that implements a feature, and it is an essential first step to any maintenance task. There are many existing techniques for feature location that incorporate various types of analyses such as static, dynamic, and textual. In this dissertation, we recognize the advantages of leveraging several types of analyses and introduce a new approach to feature location based on combining dynamic analysis, textual analysis, and web mining algorithms applied to software. The use of web mining for feature location is a novel contribution, and we show that our new techniques based on web mining are significantly more effective than the current state of the art.;After using feature location to identify a feature\u27s source code, maintenance can be completed on that feature. Impact analysis should then be performed to revalidate the system and determine which other features may have been affected by the modifications. We define three feature coupling metrics that capture the relationship between features based on structural information, textual information, and their combination. Our novel feature coupling metrics can be used for impact analysis to quantify the strength of coupling between pairs of features. We performed three empirical studies on open-source software systems to assess the feature coupling metrics and established three major results. First, there is a moderate to strong statistically significant correlation between feature coupling and faults. Second, feature coupling can be used to correctly determine about half of the other features that would be affected by a change to a given feature. Finally, we found that the metrics align with developers\u27 opinions about pairs of features that are actually coupled

Customizable Feature based Design Pattern Recognition Integrating Multiple Techniques

Die Analyse und Rückgewinnung von Architekturinformationen aus existierenden Altsystemen ist eine komplexe, teure und zeitraubende Aufgabe, was der kontinuierlich steigenden Komplexität von Software und dem Aufkommen der modernen Technologien geschuldet ist. Die Wartung von Altsystemen wird immer stärker nachgefragt und muss dabei mit den neuesten Technologien und neuen Kundenanforderungen umgehen können. Die Wiederverwendung der Artefakte aus Altsystemen für neue Entwicklungen wird sehr bedeutsam und überlebenswichtig für die Softwarebranche. Die Architekturen von Altsystemen unterliegen konstanten Veränderungen, deren Projektdokumentation oft unvollständig, inkonsistent und veraltet ist. Diese Dokumente enthalten ungenügend Informationen über die innere Struktur der Systeme. Häufig liefert nur der Quellcode zuverlässige Informationen über die Struktur von Altsystemen. Das Extrahieren von Artefakten aus Quellcode von Altsystemen unterstützt das Programmverständnis, die Wartung, das Refactoring, das Reverse Engineering, die nachträgliche Dokumentation und Reengineering Methoden. Das Ziel dieser Dissertation ist es Entwurfsinformationen von Altsystemen zu extrahieren, mit Fokus auf die Wiedergewinnung von Architekturmustern. Architekturmuster sind Schlüsselelemente, um Architekturentscheidungen aus Quellcode von Altsystemen zu extrahieren. Die Verwendung von Mustern bei der Entwicklung von Applikationen wird allgemein als qualitätssteigernd betrachtet und reduziert Entwicklungszeit und kosten. In der Vergangenheit wurden unterschiedliche Methoden entwickelt, um Muster in Altsystemen zu erkennen. Diese Techniken erkennen Muster mit unterschiedlicher Genauigkeit, da ein und dasselbe Muster unterschiedlich spezifiziert und implementiert wird. Der Lösungsansatz dieser Dissertation basiert auf anpassbaren und wiederverwendbaren Merkmal-Typen, die statische und dynamische Parameter nutzen, um variable Muster zu definieren. Jeder Merkmal-Typ verwendet eine wählbare Suchtechnik (SQL Anfragen, Reguläre Ausdrücke oder Quellcode Parser), um ein bestimmtes Merkmal eines Musters im Quellcode zu identifizieren. Insbesondere zur Erkennung verschiedener Varianten eines Musters kommen im entwickelten Verfahren statische, dynamische und semantische Analysen zum Einsatz. Die Verwendung unterschiedlicher Suchtechniken erhöht die Genauigkeit der Mustererkennung bei verschiedenen Softwaresystemen. Zusätzlich wurde eine neue Semantik für Annotationen im Quellcode von existierenden Softwaresystemen entwickelt, welche die Effizienz der Mustererkennung steigert. Eine prototypische Implementierung des Ansatzes, genannt UDDPRT, wurde zur Erkennung verschiedener Muster in Softwaresystemenen unterschiedlicher Programmiersprachen (JAVA, C/C++, C#) verwendet. UDDPRT erlaubt die Anpassung der Mustererkennung durch den Benutzer. Alle Abfragen und deren Zusammenspiel sind konfigurierbar und erlauben dadurch die Erkennung von neuen und abgewandelten Mustern. Es wurden umfangreiche Experimente mit diversen Open Source Software Systemen durchgeführt und die erzielten Ergebnisse wurden mit denen anderer Ansätze verglichen. Dabei war es möglich eine deutliche Steigerung der Genauigkeit im entwickelten Verfahren gegenüber existierenden Ansätzen zu zeigen.Recovering design information from legacy applications is a complex, expensive, quiet challenging, and time consuming task due to ever increasing complexity of software and advent of modern technology. The growing demand for maintenance of legacy systems, which can cope with the latest technologies and new business requirements, the reuse of artifacts from the existing legacy applications for new developments become very important and vital for software industry. Due to constant evolution in architecture of legacy systems, they often have incomplete, inconsistent and obsolete documents which do not provide enough information about the structure of these systems. Mostly, source code is the only reliable source of information for recovering artifacts from legacy systems. Extraction of design artifacts from the source code of existing legacy systems supports program comprehension, maintenance, code refactoring, reverse engineering, redocumentation and reengineering methodologies. The objective of approach used in this thesis is to recover design information from legacy code with particular focus on the recovery of design patterns. Design patterns are key artifacts for recovering design decisions from the legacy source code. Patterns have been extensively tested in different applications and reusing them yield quality software with reduced cost and time frame. Different techniques, methodologies and tools are used to recover patterns from legacy applications in the past. Each technique recovers patterns with different precision and recall rates due to different specifications and implementations of same pattern. The approach used in this thesis is based on customizable and reusable feature types which use static and dynamic parameters to define variant pattern definitions. Each feature type allows user to switch/select between multiple searching techniques (SQL queries, Regular Expressions and Source Code Parsers) which are used to match features of patterns with source code artifacts. The technique focuses on detecting variants of different design patterns by using static, dynamic and semantic analysis techniques. The integrated use of SQL queries, source code parsers, regular expressions and annotations improve the precision and recall for pattern extraction from different legacy systems. The approach has introduced new semantics of annotations to be used in the source code of legacy applications, which reduce search space and time for detecting patterns. The prototypical implementation of approach, called UDDPRT is used to recognize different design patterns from the source code of multiple languages (Java, C/C++, C#). The prototype is flexible and customizable that novice user can change the SQL queries and regular expressions for detecting implementation variants of design patterns. The approach has improved significant precision and recall of pattern extraction by performing experiments on number of open source systems taken as baselines for comparisons