Software evolution history is usually represented at fine granularity by commits in software repositories, and at coarse granularity by software releases. In order to gain insights on development activities and on software evolution, the information on releases is too general, whereas the information on commits is prohibitively large to be efficiently processed by a developer. This paper proposes an automatic technique for the identification of distinct phases of evolution. Such software evolution phases are characterized by similar development activities in terms of changes to entities. Therefore, our technique decomposes software evolution history to assist developers identify periods of different development activities. Our analysis technique is a search-based optimization of the best decomposition of commits from the software repository using heuristics such as classes changed in each commit, and the magnitude/importance of these changes. To validate our technique, we applied it on the evolution history of five case studies covering multiple releases over several years of development. An interesting outcome of the evaluation is that our automatic decomposition of software evolution history recovered the original decomposition in software releases.

Learning to use existing or new software libraries is a difficult task for software developers, which would impede their productivity. Much existing work has provided different techniques to mine API usage patterns from client programs in order to help developers on understanding and using existing libraries. However, considering only client programs to identify API usage patterns is a strong constraint as the client programs source code is not always available or the clients themselves do not exist yet for newly released APIs. In this paper, we propose a technique for mining Non Client-based Usage Patterns (NCBUP miner). We detect unordered API usage patterns as distinct groups of API methods that are structurally and semantically related and thus may contribute together to the implementation of a particular functionality for potential client programs. We evaluated our technique through four APIs. The obtained results are comparable to those of client-based approaches in terms of usage-patterns cohesion.

Software developers need to cope with complexity of Application Programming Interfaces (APIs) of external libraries or frameworks. However, typical APIs provide several thousands of methods to their client programs, and such large APIs are difficult to learn and use. An API method is generally used within client programs along with other methods of the API of interest. Despite this, co-usage relationships between API methods are often not documented. We propose a technique for mining Multi-Level API Usage Patterns (MLUP) to exhibit the co-usage relationships between methods of the API of interest across interfering usage scenarios. We detect multi-level usage patterns as distinct groups of API methods, where each group is uniformly used across variable client programs, independently of usage contexts. We evaluated our technique through the usage of four APIs having up to 22 client programs per API. For all the studied APIs, our technique was able to detect usage patterns that are, almost all, highly consistent and highly cohesive across a considerable variability of client programs.

Design space exploration (DSE) aims to find optimal design candidates of a domain with respect to different objectives where design candidates are constrained by complex structural and numerical restrictions. Rule-based DSE aims to find such candidates that are reachable from an initial model by applying a sequence of exploration rules. Solving a rule-based DSE problem is a difficult challenge due to the inherently dynamic nature of the problem. In the current paper, we propose to integrate multi-objective optimization techniques by using Non-dominated Sorting Genetic Algorithms (NSGA) to drive rule-based design space exploration. For this purpose, finite populations of the most promising design candidates are maintained wrt. different optimization criteria. In our context, individuals of a generation are defined as a sequence of rule applications leading from an initial model to a candidate model. Populations evolve by mutation and crossover operations which manipulate (change, extend or combine) rule execution sequences to yield new individuals. Our multi-objective optimization approach for rule-based DSE is domain independent and it is automated by tooling built on the Eclipse framework. The main added value is to seamlessly lift multi-objective optimization techniques to the exploration process preserving both domain independence and a high-level of abstraction. Design candidates will still be represented as models and the evolution of these models as rule execution sequences. Constraints are captured by model queries while objectives can be derived both from models or rule applications.

Interfaces are widely used in Java applications as central design elements for modular programming to increase program reusability and to ease maintainability of software systems. Despite the importance of interfaces and a considerable research effort that has investigated code quality and concrete classes' design, few works have investigated interfaces' design. In this paper, we empirically study interfaces' design and its impact on the design quality of implementing classes (i.e., class cohesion) analyzing twelve Java object-oriented applications. In this study we propose the "Interface-Implementations Model" that we use to adapt class cohesion metrics to assess the cohesion of interfaces based on their implementations. Moreover, we use other metrics that evaluate the conformance of interfaces to the well-known design principles "Program to an Interface, not an implementation" and "Interface Segregation Principle". The results show that software developers abide well by the interface design principles cited above, but they neglect the cohesion property. The results also show that such design practices of interfaces lead to a degraded cohesion of implementing classes, where these latter would be characterized by a worse cohesion than other classes.

Large object-oriented applications are structured over large number of packages. Packages are important but complex structural entities that may be difficult to understand since they play different development roles (i.e., class containers, code ownership basic structure, architectural elements...). Maintainers of large applications face the problem of understanding how packages are structured in general and how they relate to each others. In this paper, we present a compact visualization, named Package Surface Blueprint, that qualifies the relationships that a package has with its neighbours. A Package Surface Blueprint represents packages around the notion of package surfaces: groups of relationships according to the packages they refer to. We present two specific views one stressing the references made by a package and another showing the inheritance structure of a package. We applied the visualization on two large case studies: ArgoUML and Squeak.