The solution to subproblems of FMS planning problems require an integrated approach. The mathematical programming approaches, which are often followed, are not suitable for large problems. In this paper, we have proposed a three stage approach to solving part type selection, machine loading and part type volume determination problems. In contrast to the usual approach of maximizing the part types in each batch (or minimizing the number of batches), we have attempted to maximize the routeing¯exibility of the batches. A heuristic has been proposed for the part type selection problem and simple mathematical programs for the other two problems. The illustrative examples show that by improving the routeinḡ exibility of the batches the overall system performance has improved.

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Java is known to be a strongly type safe language, but there are some coding conventions and when these are used in some applications like persistent storage through serialization may generate unreliable or wrong output. Such cases should be caught and modified as per requirement to produce a modified safe program. This can be achieved by designing a translator tool which can catch unsafe code segments and produce a modified safe code segment. When a singleton class is serialized it is necessary to include a special method from serializable interface within it then only it gives us right result. If this method is not there within the class then it produces unpredictable results. Such results may violate type safe property of object oriented programming. Here the translator is designed using ANTLR which is going to check availability of this method in the input java file. If this method is not found then add the method and generate a type safe program at output.The same translator can be applicable for generics and their limitations. Here the translator is going to trace if there are any unchecked warnings or runtime exception then modify the input program to generate a safe program at output. This will lead to minimize limitations of java generics.

Purpose Test suite prioritization technique is the process of modifying the order in which tests run to meet certain objectives. Early fault detection and maximum coverage of source code are the main objectives of testing. There are several test suite prioritization approaches that have been proposed at the maintenance phase of software development life cycle. A few works are done on prioritizing test suites that satisfy modified condition decision coverage (MC/DC) criteria which are derived for safety-critical systems. The authors know that it is mandatory to do MC/DC testing for Level A type software according to RTCA/DO178C standards. The paper aims to discuss this issue. Design/methodology/approach This paper provides a novel method to prioritize the test suites for a system that includes MC/DC criteria along with other important criteria that ensure adequate testing. Findings In this approach, the authors generate test suites from the input Java program using concolic testing. These test suites are utilized to measure MC/DC% by using the coverage calculator algorithm. Now, use MC/DC% and the execution time of these test suites in the basic particle swarm optimization technique with a modified objective function to prioritize the generated test suites. Originality/value The proposed approach maximizes MC/DC% and minimizes the execution time of the test suites. The effectiveness of this approach is validated by experiments on 20 moderate-sized Java programs using average percentage of fault detected metric.

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The paper presents an approach to generate and optimize test sequences from the input UML activity diagram. For this, an algorithm is proposed called “Unified Modelling Language for Test Sequence Generation" (UMLTSG) that uses a search-based algorithm, named “Test Sequence Prioritization using Ant Colony Optimization" (TSP ACO) to generate and optimize test sequences. The algorithms overcome the existing limitations of handling complex decision-making activity such as conditional activity, fork activity, and join the activity. The optimization process helps to reduce the number of processing nodes that leads to minimizing the time and cost. The proposed approach experiments on a well-known application “Railway Ticket Reservation System" (RTRS). APFD metric measures the effectiveness of our approach and found that the prioritized order of test sequences achieved 20% higher APFD score. Apart from this, we have also experimented on six real life case studies and obtained an average of 52.16% reduction in redundant test paths.