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The plain 5-fluorouracil (5FU) formulations available in the market are associated with adverse effects such as skin irritation, pruritus, redness, blisters, allergy, and dryness on the site of application. The objective of the present study was to develop a liposomal emulgel of 5FU with increased skin permeability and efficacy using clove oil and eucalyptus oil along with pharmaceutically acceptable carriers, excipients, stabilizers, binders, and additives. A series of seven formulations were developed and evaluated for their entrapment efficiency, in vitro release profile, and cumulative drug release profile. The compatibility of drugs and excipients, as confirmed by FTIR (fourier-transform infrared spectroscopy) and DSC (differential scanning calorimetry) as well as SEM (scanning electron microscopy) and TEM (transmission electron microscopy) studies, revealed that the size and shape of liposomes are smooth and spherical, and the liposomes are non-aggregated. To understand their efficacy, the optimized formulations were evaluated for cytotoxicity using B16-F10 mouse skin melanoma cells. The eucalyptus oil and clove oil-containing preparation significantly produced a cytotoxic effect against a melanoma cell line. The addition of clove oil and eucalyptus oil increased the efficacy of the formulation by improving skin permeability and reducing the dose required for the anti-skin cancer activity.

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In search based test data generation, the problem of test data generation is reduced to that of function minimization or maximization.Traditionally, for branch testing, the problem of test data generation has been formulated as a minimization problem. In this paper we define an alternate maximization formulation and experimentally compare it with the minimization formulation. We use genetic algorithm and binary particle swarm optimization as the search technique and in addition to the usual operators we also employ a branch ordering strategy, memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the branch ordering strategy, memory and elitism.

In this paper we present a novel approach for fitness computation for test data generation using genetic algorithm. Fitness computation is a two-step process. In the first step a target node sequence is determined and in the second step the actual execution path is compared with the target node sequence to compute fitness. Fitness computation uses both branch and path information. Experiments indicate that the described fitness technique results in significant improvement in search performanceKeywords-program test data generation, search-based software testing, genetic algorithm I.978-1-4799-8433-6/15/$31.00