Cherif Salama

Enhanced Genetic Algorithm for MC/DC test data generation

Structural testing is concerned with the internal structures of the written software. The targeted structural coverage criteria are usually based on the criticality of the application. Modified Condition/Decision Coverage (MC/DC) is a structural coverage criterion that was introduced to the industry by NASA. Also, MC/DC comes either highly recommended or mandated by multiple standards, including ISO 26262 from the automotive industry and DO-178C from the aviation industry due to its efficiency in bug finding while maintaining a compact test suite. However, due to its complexity, huge amount of resources are dedicated to fulfilling it. Hence, automation efforts were directed to generate test data that satisfy MC/DC. Genetic Algorithms (GA) in particular showed promising results in achieving high coverage percentages. Our results show that coverage levels could be further improved using a batch of enhancements applied on the GA search.

An enhanced genetic algorithm-based timetabling system with incremental changes

Constructing a timetable is a widespread problem. Computers can be employed to solve this problem faster and to produce better solutions. Software solutions for this problem already exist and are used by some universities. However, some universities have complex types of constraints that make it hard to use most of the available software solutions. This paper introduces a software solution for the curriculum based timetabling problem with flexibility to include some complex types of constraints. In addition, the solution provided here allows for incremental changes when new constraints are added after generating the timetable. The solution is based on the genetic algorithm with some modifications to some of the operators to enhance the algorithm. The preliminary results show that it is possible to represent some complex constraint types. Also, that incremental changes can be implemented to reach a close solution faster. It is also shown that the operators of the genetic algorithm can be modified to better suit the timetabling problem and produce better results.

A hybrid cross-language name matching technique using novel modified Levenshtein Distance

Name matching is a key component in various applications in our life like record linkage and data mining applications. This process suffers from multiple complexities such as matching data from different languages or data written by people from different cultures. In this paper, we present a new modified Cross-Language Levenshtein Distance (CLLD) algorithm that supports matching names across different writing scripts and with many-to-many characters mapping. In addition, we present a hybrid cross-language name matching technique that uses phonetic matching technique mixed with our proposed CLLD algorithm to improve the overall f-measure and speed up the matching process. Our experiments demonstrate that this method substantially outperforms a number of well-known standard phonetic and approximate string similarity methods in terms of precision, recall, and f-measure.

GPU-accelerated real-time video background subtraction

Identifying objects of interest in a video sequence is a fundamental and essential part in many vision systems. A common method to achieve that goal is to perform background subtraction. For automated surveillance systems with multiple cameras, real-time background subtraction is particularly important. In this paper, we examine how to exploit GPU parallelism to accelerate the single Gaussian background subtraction algorithm achieving real-time processing of multiple concurrent videos. Experiments performed on a low end GPU showed promising results.

WikiTrends: Unstructured Wikipedia-Based Text Analytics Framework

WikiTrends is a new analytics framework for Wikipedia articles. It adds the temporal/spatial dimensions to Wikipedia to visualize the extracted information converting the big static encyclopedia to a vibrant one by enabling the generation of aggregated views in timelines or heat maps for any user-defined collection from unstructured text. Data mining techniques were applied to detect the location, start and end year of existence, gender, and entity class for 4.85 million pages. We evaluated our extractors over a small manually tagged random set of articles. Heat maps of notable football players’ counts over history or dominant occupations in some specific era are samples of WikiTrends maps while timelines can easily illustrate interesting fame battles over history between male and female actors, music genres, or even between American, Italian, and Indian films. Through information visualization and simple configurations, WikiTrends starts a new experience in answering questions through a figure.

A GPU based genetic algorithm solution for the timetabling problem

The university course timetabling problem (UCTP) is a combinatorial optimization problem of great importance for every university. This paper proposes the use of a parallel evolutionary algorithm to solve the problem and focuses on accelerating the process for specifically very large sized problems. The problem was solved using the genetic algorithm, and accelerated with the use of the Graphics Processing Units (GPUs) capabilities in order to use very large population sizes and explore the problem solution space in a much exhaustive manner. The genetic algorithm was also enhanced with the use of local search, and allowed to deal flexibly with the incremental changes of the problems constraints while maintaining the resulting solution with minimal changes. The implementation of the proposed work was tested with the ITC2007 datasets as the benchmark set.

Optimization of Generated Test Data for MC/DC

Structural coverage criteria are employed in testing according to the criticality of the application domain. Modified Condition/Decision Coverage (MC/DC) comes highly recommended by multiple standards, including, ISO 26262 and DO-178C in the automotive and avionics industries respectively. Yet, it is time and effort consuming to construct and maintain test suites that achieve high coverage percentages of MC/DC. Search based approaches were used to automate this task due to the problem complexity. Our results show that the generated test data could be minimized while maintaining the same coverage by considering that a certain test datum can satisfy multiple MC/DC test targets. This improves the maintainability of the generated test suite and saves the resources required to define their expected outputs and any part of the testing process that is repeated per test case.

Automatic Test Data Generation Targeting Hybrid Coverage Criteria

Software used in safety critical domains such as aviation and automotive has to be rigorously tested. Since exhaustive testing is not feasible, Modified Condition/Decision Coverage (MC/DC) has been introduced as an effective structural coverage alternative. However, studies have shown that complementing the test cases satisfying MC/DC to also satisfy Boundary Value Analysis (BVA) increases the bug finding rate. Hence, the industry adopted its testing processes to accommodate both. Satisfying these coverage requirements manually is very expensive and as a result many efforts were put to automate this task. Genetic algorithms (GA) have shown their effectiveness so far in this area. We propose an approach employing GA techniques and targeting hybrid coverage criteria to increase BVA in addition to MC/DC.

CGL: A domain specific language for constraint generation

Integer linear programming solvers are used to solve a wide variety of problems emerging in diverse domains. However, automatically generating the integer linear equations that are used as input for the solvers remains a challenging task. This paper proposes a domain specific language called CGL that can be used to describe the equations in a concise manner. In addition to the proposed language implementation, the syntax and semantics of CGL are formally given. The paper demonstrates the usefulness of CGL using a motivating example.

Pin-Count and Wire Length Optimization for Electrowetting-on-Dielectric Chips: A Metaheuristics-Based Routing Algorithm

Electrowetting-on-dielectric chips are gaining momentum as efficient alternatives to conventional biochemical laboratories due to their flexibility and low power consumption. In this chapter, we present a novel two-stage metaheuristic algorithm to optimize electrode interconnect routing for pin-constrained chips. The first stage models channel routing as a traveling salesman problem and solves it using the ant colony optimization algorithm. The second stage provides detailed wire routes over a grid model. The algorithm is benchmarked over a set of real-life chip specifications. On average, comparing our results to previous work, we obtain reductions of approximately 39 % and 35 % on pin-count and total wire length, respectively.