Nouh Alhindawi

Improving Feature Location by Enhancing Source Code with Stereotypes

A novel approach to improve feature location by enhancing the corpus (i.e., source code) with static information is presented. An information retrieval method, namely Latent Semantic Indexing (LSI), is used for feature location. Adding stereotype information to each method/function enhances the corpus. Stereotypes are terms that describe the abstract role of a method, for example get, set, and predicate are well-known method stereotypes. Each method in the system is automatically stereotyped via a static-analysis approach. Experimental comparisons of using LSI for feature location with, and without, stereotype information are conducted on a set of open-source systems. The results show that the added information improves the recall and precision in the context of feature location. Moreover, the use of stereotype information decreases the total effort that a developer would need to expend to locate relevant methods of the feature.

srcSlice: very efficient and scalable forward static slicing

A highly efficient lightweight forward static slicing approach is presented and evaluated. The approach does not compute the program/system dependence graph but instead dependence and control information is computed as needed while computing the slice on a variable. The result is a list of line numbers, dependent variables, aliases, and function calls that are part of the slice for all variables (both local and global) for the entire system. The method is implemented as a tool, called srcSlice, on top of srcML, an XML representation of source code. The approach is highly scalable and can generate the slices for all variables of the Linux kernel in approximately 20 min on a typical desktop. Benchmark results are compared with the CodeSurfer slicing tool from GrammaTech Inc., and the approach compares well with regard to accuracy of slices. Copyright

Towards Understanding Large-Scale Adaptive Changes from Version Histories

A case study of three open source systems undergoing large adaptive maintenance tasks is presented. The adaptive maintenance task involves migrating each system to a new version of a third party API. The changes to support the migration were spread out over multiple years for each system. The first two systems are both part of KDE, namely KOffice and Extragear/graphics. The adaptive maintenance task, for both systems, involves migrating to a new version of Qt. The third system is OpenSceneGraph that underwent a migration to a new version of OpenGL. The case study involves sifting through tens of thousands of commits to identify only those commits involved in the specific adaptive maintenance task. The object is to develop a data set that will be used for developing automated methods to identify/characterize adaptive maintenance commits.

A tracelab-based solution for identifying traceability links using LSI

An information retrieval technique, latent semantic indexing (LSI), is used to automatically identify traceability links from system documentation to program source code. The experiment is performed in the TraceLab framework. The solution provides templates and components for building and querying LSI space and datasets (corpora) that can be used as inputs for these components. The proposed solution is evaluated on traceability links already discovered by mining adaptive commits of the open source system KDE/Koffice. The results show that the approach can identify of traceability links with high precision using TraceLab components.

Hybrid SMOTE-Ensemble Approach for Software Defect Prediction

Software defect prediction is the process of identifying new defects/bugs in software modules. Software defect presents an error in a computer program, which is caused by incorrect code or incorrect programming logic. As a result, undiscovered defects lead to a poor quality software products. In recent years, software defect prediction has received a considerable amount of attention from researchers. Most of the previous defect detection algorithms are marred by low defect detection ratios. Furthermore, software defect prediction is very challenging problem due to the high imbalanced distribution, where the bug-free codes are much higher than defective ones. In this paper, the software defect prediction problem is formulated as a classification task, and then it examines the impact of several ensembles methods on the classification effectiveness. In addition, the best ensemble classifier will be selected to be trained again on an over-sampled datasets using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm to tackle imbalanced distribution problem. The proposed hybrid method is evaluated using four software defects datasets. Experimental results demonstrate that the proposed method can effectively enhance the defect prediction accuracy.

Parallel coordinates metrics for classification visualization

The high dimensionality of data presents a major issue in understanding and interpreting the results of classification learning. Among the various approaches that address this issue, parallel coordinates visualization has proven its capabilities to enhance investigation and comprehension of data dimension features especially when the number of dimensions is high and there are numerous output classes. We propose several parallel coordinates metrics, namely entropy, class ordering, and edge crossing, to further facilitate inspection of data features and their relevance to output class. Experiments on real world datasets are presented to show the effectiveness of the proposed approach.

A novel video encryption algorithm-based on speaker voice as the public key

Advances in the digital content transmission have been extremely increased in the past few years. In addition, the security and privacy issues of the transmitted data have increasingly an important concern in multimedia technology. In this paper, we propose applicable and secure video encryption algorithm. This makes secure video encryption feasible for realtime application. The proposed algorithm concentrates in general on the symmetric key cryptographic technique. Characteristically, the multimedia technology which has been usually used by public users is resulting in a key exchange problem and normally a trusted intermediate authority takes this responsibility. In this paper, we have proposed a novel video encryption algorithm based on the speaker voice to generate a public key which eliminates the need for a trusted third party for key exchange. The generated public key from the speakers voice is used to encrypt of the transferred digital multimedia over the open communication channel (i.e. Internet). The experimental results showed that the proposed algorithm is very secure and has on average only 5.4 ms of encryption time per frame, which is significantly smaller compared to the state of the art secure video encryption algorithms.

Image encryption algorithm based on Lorenz chaotic map with dynamic secret keys

Over the past two decades, chaos-based encryption appeared as an original application for nonlinear dynamics and deterministic chaos to encrypt and decrypt data. In this paper, an implementation of digital image encryption scheme based on the Lorenz chaotic system is proposed. While in the process of generating chaotic key stream, the hash value of the plain image is embedded in the proposed cryptosystem to dynamically alter the initial secret keys to increase the security level. The proposed digital image encryption algorithm is described in detail along with its security analysis and implementation. The experimental results show that the proposed digital image encryption algorithm is efficient and has high security features and is suitable for practical uses across insecure networks.

Hybrid Technique for Java Code Complexity Analysis

Software complexity can be defined as the degree of difficulty in analysis, testing, design and implementation of software. Typically, reducing model complexity has a significant impact on maintenance activities. A lot of metrics have been used to measure the complexity of source code such as Halstead, McCabe Cyclomatic, Lines of Code, and Maintainability Index, etc. This paper proposed a hybrid module which consists of two theories which are Halstead and McCabe, both theories will be used to analyze a code written in Java. The module provides a mechanism to better evaluate the proficiency level of programmers, and also provides a tool which enables the managers to evaluate the programming levels and their enhancements over time. This will be known by discovering the various differences between levels of complexity in the code. If the program complexity level is low, then of the programmer professionalism level is high, on the other hand, if the program complexity level is high, then the programmer professionalism level is almost low. The results of the conducted experiments show that the proposed approach give very high and accurate evaluation for the undertaken systems.

A Topic Modeling Based Solution for Confirming Software Documentation Quality

this paper presents an approach for evaluating and confirming the quality of the external software documentation using topic modeling. Typically, the quality of the external documentation has to mirror precisely the organization of the source code. Therefore, the elements of such documentation should be strongly written, associated, and presented. In this paper, we use Latent Dirichlet Allocation (LDA) and HELLINGER DISTANCE to compute the similarities between the fragments of source code and the external documentation topics. These similarities are used in this paper to improve and advance the existing external documentation. Furthermore, these similarities can also be used for evaluating the new documenting process during the evolution phase of the software. The results show that the new approach yields state-of-the-art performance in evaluating and confirming the existing external documentations quality and superiority.