On the Effect of Semantically Enriched Context Models on Software Modularization

Many of the existing approaches for program comprehension rely on the linguistic information found in source code, such as identifier names and comments. Semantic clustering is one such technique for modularization of the system that relies on the informal semantics of the program, encoded in the vocabulary used in the source code. Treating the source code as a collection of tokens loses the semantic information embedded within the identifiers. We try to overcome this problem by introducing context models for source code identifiers to obtain a semantic kernel, which can be used for both deriving the topics that run through the system as well as their clustering. In the first model, we abstract an identifier to its type representation and build on this notion of context to construct contextual vector representation of the source code. The second notion of context is defined based on the flow of data between identifiers to represent a module as a dependency graph where the nodes correspond to identifiers and the edges represent the data dependencies between pairs of identifiers. We have applied our approach to 10 medium-sized open source Java projects, and show that by introducing contexts for identifiers, the quality of the modularization of the software systems is improved. Both of the context models give results that are superior to the plain vector representation of documents. In some cases, the authoritativeness of decompositions is improved by 67%. Furthermore, a more detailed evaluation of our approach on JEdit, an open source editor, demonstrates that inferred topics through performing topic analysis on the contextual representations are more meaningful compared to the plain representation of the documents. The proposed approach in introducing a context model for source code identifiers paves the way for building tools that support developers in program comprehension tasks such as application and domain concept location, software modularization and topic analysis

Subsequences and Supersequences of Strings

Stringology - the study of strings - is a branch of algorithmics which been the sub-ject of mounting interest in recent years. Very recently, two books [M. Crochemore and W. Rytter, Text Algorithms, Oxford University Press, 1995] and [G. Stephen, String Searching Algorithms, World Scientific, 1994] have been published on the subject and at least two others are known to be in preparation. Problems on strings arise in information retrieval, version control, automatic spelling correction, and many other domains. However the greatest motivation for recent work in stringology has come from the field of molecular biology. String problems occur, for example, in genetic sequence construction, genetic sequence comparison, and phylogenetic tree construction. In this thesis we study a variety of string problems from a theoretical perspective. In particular, we focus on problems involving subsequences and supersequences of strings

Fitness Proportionate Niching: Harnessing The Power Of Evolutionary Algorithms For Evolving Cooperative Populations And Dynamic Clustering

Evolutionary algorithms work on the notion of best fit will survive criteria. This makes evolving a cooperative and diverse population in a competing environment via evolutionary algorithms a challenging task. Analogies to species interactions in natural ecological systems have been used to develop methods for maintaining diversity in a population. One such area that mimics species interactions in natural systems is the use of niching. Niching methods extend the application of EAs to areas that seeks to embrace multiple solutions to a given problem. The conventional fitness sharing technique has limitations when the multimodal fitness landscape has unequal peaks. Higher peaks are strong population attractors. And this technique suffers from the curse of population size in attempting to discover all optimum points. The use of high population size makes the technique computationally complex, especially when there is a big jump in fitness values of the peaks. This work introduces a novel bio-inspired niching technique, termed Fitness Proportionate Niching (FPN), based on the analogy of finite resource model where individuals share the resource of a niche in proportion to their actual fitness. FPN makes the search algorithm unbiased to the variation in fitness values of the peaks and hence mitigates the drawbacks of conventional fitness sharing. FPN extends the global search ability of Genetic Algorithms (GAs) for evolving hierarchical cooperation in genetics-based machine learning and dynamic clustering. To this end, this work introduces FPN based resource sharing which leads to the formation of a viable default hierarchy in classifiers for the first time. It results in the co-evolution of default and exception rules, which lead to a robust and concise model description. The work also explores the feasibility and success of FPN for dynamic clustering. Unlike most other clustering techniques, FPN based clustering does not require any a priori information on the distribution of the data

A Configuration Management System for Software Product Lines

Software product line engineering (SPLE) is a methodology for developing a family of software products in a particular domain by systematic reuse of shared code in order to improve product quality and reduce development time and cost. Currently, there are no software configuration management (SCM) tools that support software product line evolution. Conventional SCM tools are designed to support single product development. The use of conventional SCM tools forces developers to treat a software product line as a single software project by introducing new programming language constructs or using conditional compilation. We propose a research conguration management prototype called Molhado SPL that is designed specifically to support the evolution of software product lines. Molhado SPL addresses the evolution problem at the configuration level instead of at the code level. We studied the type of operations needed to support the evolution of software product lines and proposed a versioning model and eight cases of change propagation. Molhado SPL supports independent evolution of core assets and products, the sharing of code and the tracking relationships between products and shared code, and the eight cases of change propagation. The Molhado SPL consists of four layers with each layer providing a different type of service. At the heart of Molhado SPL are the versioning model, component object, shared component object, and project objects that allow for independent evolution of products and shared artifacts, for sharing, and for supporting change propagation. Furthermore,they allow product specific changes to shared code without interfering with the core asset that is shared. Products can also introduce product specific assets that only exist in that product. In order to for Molhado SPL to support product line, we implemented XML merging, feature model editing and debugging, and version-aware XML documents. To support merging of XML documents, we implemented a 3-way XML document merging algorithm that uses versioned data structures, change detection, and node identity. To support software product line derivation or modeling of software product line, we implemented support for feature model including editing and debugging. Finally, we created the version-aware XML document framework to support collaborative editing of XML documents without requiring a version repository. The version history is embedded in the documents using XML namespaces, so that the documents remain valid under the XML specification. The version-aware XML framework can also be used to support the exporting of documents from Molhado SPL repository to be edit outside and import back the change history made to the document. We evaluated Molhado SPL with two product lines: a document product line and a the graph data structures product line. This evaluation showed that Molhado SPL supports independently evolution of products and core assets and the eight change propagation cases. We did not evaluate MolhadoSPL in terms of scalability or usability. The main contributions of this dissertation research are: 1) Molhado SPL that supports the evolution of product lines, 2) a fast 3-way XML merge algorithm, 3) a version-aware XML document framework, and 4) a feature model editor and debugger

Human-Robot Collaboration in Automotive Assembly

In the past decades, automation in the automobile production line has significantly increased the efficiency and quality of automotive manufacturing. However, in the automotive assembly stage, most tasks are still accomplished manually by human workers because of the complexity and flexibility of the tasks and the high dynamic unconstructed workspace. This dissertation is proposed to improve the level of automation in automotive assembly by human-robot collaboration (HRC). The challenges that eluded the automation in automotive assembly including lack of suitable collaborative robotic systems for the HRC, especially the compact-size high-payload mobile manipulators; teaching and learning frameworks to enable robots to learn the assembly tasks, and how to assist humans to accomplish assembly tasks from human demonstration; task-driving high-level robot motion planning framework to make the trained robot intelligently and adaptively assist human in automotive assembly tasks. The technical research toward this goal has resulted in several peer-reviewed publications. Achievements include: 1) A novel collaborative lift-assist robot for automotive assembly; 2) Approaches of vision-based robot learning of placing tasks from human demonstrations in assembly; 3) Robot learning of assembly tasks and assistance from human demonstrations using Convolutional Neural Network (CNN); 4) Robot learning of assembly tasks and assistance from human demonstrations using Task Constraint-Guided Inverse Reinforcement Learning (TC-IRL); 5) Robot learning of assembly tasks from non-expert demonstrations via Functional Objective-Oriented Network (FOON); 6) Multi-model sampling-based motion planning for trajectory optimization with execution consistency in manufacturing contexts. The research demonstrates the feasibility of a parallel mobile manipulator, which introduces novel conceptions to industrial mobile manipulators for smart manufacturing. By exploring the Robot Learning from Demonstration (RLfD) with both AI-based and model-based approaches, the research also improves robots’ learning capabilities on collaborative assembly tasks for both expert and non-expert users. The research on robot motion planning and control in the dissertation facilitates the safety and human trust in industrial robots in HRC

Adaptive rule-based malware detection employing learning classifier systems

Efficient and accurate malware detection is increasingly becoming a necessity for society to operate. Existing malware detection systems have excellent performance in identifying known malware for which signatures are available, but poor performance in anomaly detection for zero day exploits for which signatures have not yet been made available or targeted attacks against a specific entity. The primary goal of this thesis is to provide evidence for the potential of learning classier systems to improve the accuracy of malware detection. A customized system based on a state-of-the-art learning classier system is presented for adaptive rule-based malware detection, which combines a rule-based expert system with evolutionary algorithm based reinforcement learning, thus creating a self-training adaptive malware detection system which dynamically evolves detection rules. This system is analyzed on a benchmark of malicious and non-malicious files. Experimental results show that the system can outperform C4.5, a well-known non-adaptive machine learning algorithm, under certain conditions. The results demonstrate the system\u27s ability to learn effective rules from repeated presentations of a tagged training set and show the degree of generalization achieved on an independent test set. This thesis is an extension and expansion of the work published in the Security, Trust, and Privacy for Software Applications workshop in COMPSAC 2011 - the 35th Annual IEEE Signature Conference on Computer Software and Applications --Abstract, page iii