A Survey on Software Testing Techniques using Genetic Algorithm

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.Comment: 13 Page

Abiraterone acetate plus prednisolone with or without enzalutamide for patients with metastatic prostate cancer starting androgen deprivation therapy: final results from two randomised phase 3 trials of the STAMPEDE platform protocol

Background: Abiraterone acetate plus prednisolone (herein referred to as abiraterone) or enzalutamide added at the start of androgen deprivation therapy improves outcomes for patients with metastatic prostate cancer. Here, we aimed to evaluate long-term outcomes and test whether combining enzalutamide with abiraterone and androgen deprivation therapy improves survival. Methods: We analysed two open-label, randomised, controlled, phase 3 trials of the STAMPEDE platform protocol, with no overlapping controls, conducted at 117 sites in the UK and Switzerland. Eligible patients (no age restriction) had metastatic, histologically-confirmed prostate adenocarcinoma; a WHO performance status of 0–2; and adequate haematological, renal, and liver function. Patients were randomly assigned (1:1) using a computerised algorithm and a minimisation technique to either standard of care (androgen deprivation therapy; docetaxel 75 mg/m2 intravenously for six cycles with prednisolone 10 mg orally once per day allowed from Dec 17, 2015) or standard of care plus abiraterone acetate 1000 mg and prednisolone 5 mg (in the abiraterone trial) orally or abiraterone acetate and prednisolone plus enzalutamide 160 mg orally once a day (in the abiraterone and enzalutamide trial). Patients were stratified by centre, age, WHO performance status, type of androgen deprivation therapy, use of aspirin or non-steroidal anti-inflammatory drugs, pelvic nodal status, planned radiotherapy, and planned docetaxel use. The primary outcome was overall survival assessed in the intention-to-treat population. Safety was assessed in all patients who started treatment. A fixed-effects meta-analysis of individual patient data was used to compare differences in survival between the two trials. STAMPEDE is registered with ClinicalTrials.gov (NCT00268476) and ISRCTN (ISRCTN78818544). Findings: Between Nov 15, 2011, and Jan 17, 2014, 1003 patients were randomly assigned to standard of care (n=502) or standard of care plus abiraterone (n=501) in the abiraterone trial. Between July 29, 2014, and March 31, 2016, 916 patients were randomly assigned to standard of care (n=454) or standard of care plus abiraterone and enzalutamide (n=462) in the abiraterone and enzalutamide trial. Median follow-up was 96 months (IQR 86–107) in the abiraterone trial and 72 months (61–74) in the abiraterone and enzalutamide trial. In the abiraterone trial, median overall survival was 76·6 months (95% CI 67·8–86·9) in the abiraterone group versus 45·7 months (41·6–52·0) in the standard of care group (hazard ratio [HR] 0·62 [95% CI 0·53–0·73]; p<0·0001). In the abiraterone and enzalutamide trial, median overall survival was 73·1 months (61·9–81·3) in the abiraterone and enzalutamide group versus 51·8 months (45·3–59·0) in the standard of care group (HR 0·65 [0·55–0·77]; p<0·0001). We found no difference in the treatment effect between these two trials (interaction HR 1·05 [0·83–1·32]; pinteraction=0·71) or between-trial heterogeneity (I2 p=0·70). In the first 5 years of treatment, grade 3–5 toxic effects were higher when abiraterone was added to standard of care (271 [54%] of 498 vs 192 [38%] of 502 with standard of care) and the highest toxic effects were seen when abiraterone and enzalutamide were added to standard of care (302 [68%] of 445 vs 204 [45%] of 454 with standard of care). Cardiac causes were the most common cause of death due to adverse events (five [1%] with standard of care plus abiraterone and enzalutamide [two attributed to treatment] and one (<1%) with standard of care in the abiraterone trial). Interpretation: Enzalutamide and abiraterone should not be combined for patients with prostate cancer starting long-term androgen deprivation therapy. Clinically important improvements in survival from addition of abiraterone to androgen deprivation therapy are maintained for longer than 7 years. Funding: Cancer Research UK, UK Medical Research Council, Swiss Group for Clinical Cancer Research, Janssen, and Astellas

A novel approach for deriving interactions for combinatorial testing

Combinatorial testing focuses on identifying faults that arise due to interaction of values of a small number of input parameters. Also known as t-way testing, it reduces the size of test set by selecting a minimal set of test cases that cover all the possible t-way tuples. An optimal value of t (degree of interaction) for t-way testing for the system would maximize fault detection count in minimal number of test cases. However, identification of an optimal value of for t-way testing for the system remains an open issue. In this paper, we present an approach to identify the interactions that exist in the source code, thereby reducing the count of interactions to be tested. DD path graph is generated from the source code and interactions are identified using data flow techniques. Two case studies are also discussed in order to demonstrate our approach. Experimental results indicate that our approach significantly reduces the count of interactions to be tested without significant loss of fault detection capability. The approach is extensible to large sized structured programs

ABC-CAG: Covering Array Generator for Pair-wise Testing Using Artificial Bee Colony Algorithm

Testing is an indispensable part of the software development life cycle. It is performed to improve the performance, quality and reliability of the software. Various types of testing such as functional testing and structural testing are performed on software to uncover the faults caused by incorrect code, interaction of input parameters etc. One of the major factors in deciding the quality of testing is the design of relevant test cases which is very crucial for the success of testing. In this paper we concentrate on generating test cases to uncover faults caused by the interaction of input parameters. It is advisable to perform thorough testing but the number of test cases grows exponentially with the increase in number of input parameters, which makes exhaustive testing of interaction of input parameters imprudent. An alternative to exhaustive testing is combinatorial interaction testing (CIT) which requires that every t-way interaction of input parameters be covered by at least one test case. Here, we present a novel strategy ABC-CAG (Artificial Bee Colony-Covering Array Generator) based on Artificial Bee Colony (ABC) algorithm to generate covering array and mixed covering array for pair-wise testing. The proposed ABC-CAG strategy is implemented in a tool and experiments are conducted on various benchmark problems to evaluate the efficacy of the proposed approach. Experimental results show that ABC-CAG generates better/comparable results as compared to the existing state-of-the-art algorithms