Software Evolution

Automatic Quality Assessment of Source Code Comments: The JavadocMiner


An important software engineering artefact used by developers and maintainers to assist in software comprehension and maintenance is source code documentation. It provides insights that help software engineers to effectively perform their tasks, and therefore ensuring the quality of the documentation is extremely important. Inline documentation is at the forefront of explaining a programmer's original intentions for a given implementation. Since this documentation is written in natural language, ensuring its quality needs to be performed manually. In this paper, we present an effective and automated approach for assessing the quality of inline documentation using a set of heuristics, targeting both quality of language and consistency between source code and its comments. We apply our tool to the different modules of two open source applications (ArgoUML and Eclipse), and correlate the results returned by the analysis with bug defects reported for the individual modules in order to determine connections between documentation and code quality.

Generating an NLP Corpus from Java Source Code: The SSL Javadoc Doclet


Source code contains a large amount of natural language text, particularly in the form of comments, which makes it an emerging target of text analysis techniques. Due to the mix with program code, it is difficult to process source code comments directly within NLP frameworks such as GATE. Within this work we present an effective means for generating a corpus using information found in source code and in-line documentation, by developing a custom doclet for the Javadoc tool. The generated corpus uses a schema that is easily processed by NLP applications, which allows language engineers to focus their efforts on text analysis tasks, like automatic quality control of source code comments. The SSLDoclet is available as open source software.

A Quality Perspective of Evolvability Using Semantic Analysis


Software development and maintenance are highly distributed processes that involve a multitude of supporting tools and resources. Knowledge relevant to these resources is typically dispersed over a wide range of artifacts, representation formats, and abstraction levels. In order to stay competitive, organizations are often required to assess and provide evidence that their software meets the expected requirements. In our research, we focus on assessing non-functional quality requirements, specifically evolvability, through semantic modeling of relevant software artifacts. We introduce our SE-Advisor that supports the integration of knowledge resources typically found in software ecosystems by providing a unified ontological representation. We further illustrate how our SE-Advisor takes advantage of this unified representation to support the analysis and assessment of different types of quality attributes related to the evolvability of software ecosystems.

Beyond Information Silos — An Omnipresent Approach to Software Evolution


Nowadays, software development and maintenance are highly distributed processes that involve a multitude of supporting tools and resources. Knowledge relevant for a particular software maintenance task is typically dispersed over a wide range of artifacts in different representational formats and at different abstraction levels, resulting in isolated 'information silos'. An increasing number of task-specific software tools aim to support developers, but this often results in additional challenges, as not every project member can be familiar with every tool and its applicability for a given problem. Furthermore, historical knowledge about successfully performed modifications is lost, since only the result is recorded in versioning systems, but not how a developer arrived at the solution. In this research, we introduce conceptual models for the software domain that go beyond existing program and tool models, by including maintenance processes and their constituents. The models are supported by a pro-active, ambient, knowledge-based environment that integrates users, tasks, tools, and resources, as well as processes and history-specific information. Given this ambient environment, we demonstrate how maintainers can be supported with contextual guidance during typical maintenance tasks through the use of ontology queries and reasoning services.

Story-driven Approach to Software Evolution


From a maintenance perspective, only software that is well understood can evolve in a controlled and high-quality manner. Software evolution itself is a knowledge-driven process that requires the use and integration of different knowledge resources. The authors present a formal representation of an existing process model to support the evolution of software systems by representing knowledge resources and the process model using a common representation based on ontologies and description logics. This formal representation supports the use of reasoning services across different knowledge resources, allowing for the inference of explicit and implicit relations among them. Furthermore, an interactive story metaphor is introduced to guide maintainers during their software evolution activities and to model the interactions between the users, knowledge resources and process model.

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