Bryan F. Jones

Search-based software engineering

Abstract This paper claims that a new field of software engineering research and practice is emerging: search-based software engineering. The paper argues that software engineering is ideal for the application of metaheuristic search techniques, such as genetic algorithms, simulated annealing and tabu search. Such search-based techniques could provide solutions to the difficult problems of balancing competing (and some times inconsistent) constraints and may suggest ways of finding acceptable solutions in situations where perfect solutions are either theoretically impossible or practically infeasible. In order to develop the field of search-based software engineering, a reformulation of classic software engineering problems as search problems is required. The paper briefly sets out key ingredients for successful reformulation and evaluation criteria for search-based software engineering.

A reappraisal of the use of infrared thermal image analysis in medicine

Infrared thermal imaging of the skin has been used for several decades to monitor the temperature distribution of human skin. Abnormalities such as malignancies, inflammation, and infection cause localized increases in temperature which show as hot spots or as asymmetrical patterns in an infrared thermogram. Even though it is nonspecific, infrared thermology is a powerful detector of problems that affect a patients physiology. While the use of infrared imaging is increasing in many industrial and security applications, it has declined in medicine probably because of the continued reliance on first generation cameras. The transfer of military technology for medical use has prompted this reappraisal of infrared thermology in medicine. Digital infrared cameras have much improved spatial and thermal resolutions, and libraries of image processing routines are available to analyze images captured both statically and dynamically. If thermographs are captured under controlled conditions, they may be interpreted readily to diagnose certain conditions and to monitor the reaction of a patients physiology to thermal and other stresses. Some of the major areas where infrared thermography is being used successfully are neurology, vascular disorders, rheumatic diseases, tissue viability, oncology (especially breast cancer), dermatological disorders, neonatal, ophthalmology, and surgery.

Automatic structural testing using genetic algorithms

Genetic algorithms have been used to generate test sets automatically by searching the domain of the software for suitable values to satisfy a predefined testing criterion. These criteria have been set by the requirements for test data set adequacy of structural testing, such as obtaining full branch coverage and controlling the number of iterations of a conditional loop. This technique has been applied successfully to several problems, varying in complexity from a quadratic equation solver to a generic sort module that comprises several procedures. In these cases, full branch coverage was obtained. Genetic algorithms could be applied to approaches other than structural testing, provided that the goal of the testing is clearly defined, and a fitness function which relates to this goal can be devised to give a single numeric value for the fitness. The quality of the test data is enhanced by designing the fitness function to generate data close to a subdomain boundary where the likelihood of revealing an error is higher.

Testing real-time systems using genetic algorithms

The development of real-time systems is an essential industrial activity whose importance is increasing. The most important analytical method to assure the quality of real-time systems is dynamic testing. Testing is the only method which examines the actual run-time behaviour of real-time software, based on an execution in the real application environment. Dynamic aspects like the duration of computations, the memory actually needed, or the synchronization of parallel processes are of major importance for the correct function of real-time systems and have to be tested. A comprehensive investigation of existing software test methods shows that they mostly concentrate on testing for functional correctness. They are not suited for an examination of temporal correctness which is essential to real-time systems. Very small systems show a wide range of different execution times. Therefore, existing test procedures must be supplemented by new methods, which concentrate on determining whether the system violates its specified timing constraints. In general, this means that outputs are produced too early or their computation takes too long. The task of the tester is to find the inputs with the longest or shortest execution times to check whether they produce a temporal error. If the search for such inputs is interpreted as a problem of optimization, genetic algorithms can be used to find the inputs with the longest or shortest execution times automatically. The fitness function is the execution time measured in processor cycles. Experiments using genetic algorithms on a number of programs with up to 1511 LOC and 843 integer input parameters have successfully identified new longer and shorter paths than had been found using random testing or systematic testing. Genetic algorithms are able therefore to check large programs and they show considerable promise in establishing the validity of the temporal behaviour of real-time software.

A Strategy for using Genetic Algorithms to Automate Branch and Fault-based Testing

Genetic algorithms have been used successfully to generate software test data automatically; all branches were covered with substantially fewer generated tests than simple random testing. We generated test sets which executed all branches in a variety of programs including a quadratic equation solver, remainder, linear and binary search procedures, and a triangle classifier comprising a system of five procedures. We regard the generation of test sets as a search through the input domain for appropriate inputs. The genetic algorithms generated test data to give 100% branch coverage in up to two orders of magnitude fewer tests than random testing. Whilst some of this benefit is offset by increased computation effort, the adequacy of the test data is improved by the genetic algorithms ability to generate test sets which are at or close to the input subdomain boundaries. Genetic algorithms may be used for fault-based testing where faults associated with mistakes in branch predicates are revealed. The software has been deliberately seeded with faults in the branch predicates (i.e. mutation testing), and our system successfully killed 97% of the mutants.

A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms

Abstract Location of fire stations is an important factor in its fire protection capability. This paper aims to determine the optimal location of fire station facilities. The proposed method is the combination of a fuzzy multi-objective programming and a genetic algorithm. The original fuzzy multiple objectives are appropriately converted to a single unified ‘min–max’ goal, which makes it easy to apply a genetic algorithm for the problem solving. Compared with the existing methods of fire station location our approach has three distinguish features: (1) considering fuzzy nature of a decision maker (DM) in the location optimization model; (2) fully considering the demands for the facilities from the areas with various fire risk categories; (3) being more understandable and practical to DM. The case study was based on the data collected from the Derbyshire fire and rescue service and used to illustrate the application of the method for the optimization of fire station locations.

An instrument to measure the dimensions of skin wounds

A new instrument based on the principle of color-coded structured light has been developed to measure the area, volume, and color of skin ulcers and pressure sores. A set of parallel stripes of alternating colors is projected onto the ulcerated skin and recorded by a CCD camera. The color, width, distance, and coding of the stripes have been adjusted to maximize the precision of the instrument. Algorithms have been developed to extract the stripes and determine their centers to better than 0.1 mm, even under circumstances where the skin reflectivity varies widely over short distances. A depth map for the ulcerated skin is calculated by triangulation. The volume of the ulcer is that sandwiched between the base of the lesion and the original healthy skin, which is simulated by a cubic spline interpolation between the surrounding areas of healthy skin. The instrument measures the ulcers volume with a precision of about 5% provided the ratio of the ulcers volume to area is greater than 0.4 cm. The technique has been used in hospital clinics for a wide variety of wounds. >

Genetic algorithm based software integration with minimum software risk

This paper investigates an approach of integrating software with a minimum risk using Genetic Algorithms (GA). The problem was initially proposed by the need of sharing common software components among various departments within a same organization. Two significant contributions have been made in this study: (1) an assimilation exchange based software integration approach is proposed; (2) the software integration problem is formulated as a search problem and solved by using a GA. A case study is based on an on-going software integration project carried out in the Derbyshire Fire Rescue Service, and used to illustrate the application of the approach.

Software engineering using metaheuristic innovative algorithms: workshop report

Abstract This paper reports on the first International Workshop on Software Engineering using Metaheuristic Innovative Algorithms, which was held in Toronto on the 14th of May 2001 as a part of the IEEE International Conference on Software Engineering.

Content-based image retrieval for medical infrared images

Past efforts on the automated processing on medical infrared images has typically focused on specialized applications like the detection of breast cancer. We propose the application of content-based image retrieval (CBIR) to medical thermal images. CBIR allows the retrieval of similar images based on features directly extracted from the image data. Hence, image retrieval for a thermal image that shows symptoms of a certain disease will provide visually similar cases which will usually also represent similarities in medical terms. The image features we propose for this purpose are a set of moment invariants of the grayscale thermal images.