Hani Abdeen

Automatic Package Coupling and Cycle Minimization

Object-oriented (OO) software is usually organized into subsystems using the concepts of package or module.Such modular structure helps applications to evolve when facing new requirements.However, studies show that as software evolves to meet requirements and environment changes, modularization quality degrades. To help maintainers improve the quality of software modularization we have designed and implemented a heuristic search-based approach for automatically optimizing inter-package connectivity (i.e., dependencies).In this paper, we present our approach and its underlying techniques and algorithm.We show through a case study how it enables maintainers to optimize OO package structure of source code.Our optimization approach is based on Simulated Annealing technique.

Package Surface Blueprints: Visually Supporting the Understanding of Package Relationships

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.

Towards automatically improving package structure while respecting original design decisions

Recently, there has been an important progress in applying search-based optimization techniques to the problem of software re-modularization. Yet, a major part of the existing body of work addresses the problem of modularizing software systems from scratch, regardless of the existing packages structure. This paper presents a novel multi-objective optimization approach for improving existing packages structure. The optimization approach aims at increasing the cohesion and reducing the coupling and cyclic connectivity of packages, by modifying as less as possible the existing packages organization. Moreover, maintainers can specify several constraints to guide the optimization process with regard to extra design factors. To this contribution, we use the Non-Dominated Sorting Genetic Algorithm (NSGA-II). We evaluate the optimization approach through an experiment covering four real-world software systems. The results promise the effectiveness of our optimization approach for improving existing packages structure by doing very small modifications.

Modularization Metrics: Assessing Package Organization in Legacy Large Object-Oriented Software

There exist many large object-oriented software systems consisting of several thousands of classes that are organized into several hundreds of packages. In such software systems, classes cannot be considered as units for software modularization. In such context, packages are not simply classes containers, but they also play the role of modules: a package should focus to provide well identified services to the rest of the software system. Therefore, understanding and assessing package organization is primordial for software maintenance tasks. Although there exist a lot of works proposing metrics for the quality of a single class and/or the quality of inter-class relationships, there exist few works dealing with some aspects for the quality of package organization and relationship. We believe that additional investigations are required for assessing package modularity aspects. The goal of this paper is to provide a complementary set of metrics that assess some modularity principles for packages in large legacy object-oriented software: Information-Hiding, Changeability and Reusability principles. Our metrics are defined with respect to object-oriented dependencies that are caused by inheritance and method call. We validate our metrics theoretically through a careful study of the mathematical properties of each metric.

Multi-objective optimization in rule-based design space exploration

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.

Mining Multi-level API Usage Patterns

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.

Package Fingerprints: A visual summary of package interface usage

Context: Object-oriented languages such as Java, Smalltalk, and C++ structure their programs using packages. Maintainers of large systems need to understand how packages relate to each other, but this task is complex because packages often have multiple clients and play different roles (class container, code ownership, etc.). Several approaches have been proposed, among which the use of cohesion and coupling metrics. Such metrics help identify candidate packages for restructuring; however, they do not help maintainers actually understand the structure and interrelationships between packages. Objectives: In this paper, we use pre-attentive processing as the basis for package visualization and see to what extent it could be used in package understanding. Method: We present the Package Fingerprint, a 2D visualization of the references made to and from a package. The proposed visualization offers a semantically rich, but compact and zoomable views centered on packages. We focus on two views (incoming and outgoing references) that help users understand how the package under analysis is used by the system and how it uses the system. Results: We applied these views on four large systems: Squeak, JBoss, Azureus, and ArgoUML. We obtained several interesting results, among which, the identification of a set of recurring visual patterns that help maintainers: (a) more easily identify the role of and the way a package is used within the system (e.g., the package under analysis provides a set of layered services), and (b) detect either problematic situations (e.g., a single package that groups together a large number of basic services) or opportunities for better package restructuring (e.g., removing cyclic dependencies among packages). The visualization generally scaled well and the detection of different patterns was always possible. Conclusion: The proposed visualizations and patterns proved to be useful in understanding and maintaining the different systems we addressed. To generalize to other contexts and systems, a real user study is required.

Could we infer unordered API usage patterns only using the library source code

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.

Detection of software evolution phases based on development activities

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 dependency-based change impact predictors using independent change histories

Context: Recent studies showed that combining present data, which are derived from the current software version, with past data, which are derived from previous software versions, can improve the accuracy of change impact predictions. However, for a specific program, existing combined techniques can rely only on version history of that program, if available, and the prediction results depend on the variety of available change impact examples.Objective: We propose a hybrid probabilistic approach that predicts the change impact for a software entity using, as training data, existing version histories of whatever software systems.Method: Change-impact predictors are learned from past change impact graphs (CIGs), extracted from the version history, along with their associations with different influencing factors of change propagation. The learning examples in CIGs are not specific to the software entities that are covered in those examples, and the change propagation influencing factors are structural and conceptual dependencies between software entities. Once our predictors are trained, they can predict change impacts regardless of the version history of the software under-analysis. We evaluate our approach using four systems in two scenarios. First, we use as training data the CIGs extracted from previous versions of the system under-analysis. Second, for each analyzed system, we use only the training data extracted from the other systems.Results: Our approach produces accurate predictions in terms of both precision and recall. Moreover, when training our classifiers with a large variety of CIGs extracted from the change histories of different projects, the recall scores of predicted impact sets were significantly improved.Conclusion:Our approach produces accurate predictions for new classes without recorded change histories, as well as for old classes. For the systems considered in our evaluation, once our approach is trained with a variety of CIGs it can predict change impacts with good recall scores.