Test oracle assessment and improvement

We introduce a technique for assessing and improving test oracles by reducing the incidence of both false positives and false negatives. We prove that our approach can always result in an increase in the mutual information between the actual and perfect oracles. Our technique combines test case generation to reveal false positives and mutation testing to reveal false negatives. We applied the decision support tool that implements our oracle improvement technique to five real-world subjects. The experimental results show that the fault detection rate of the oracles after improvement increases, on average, by 48.6% (86% over the implicit oracle). Three actual, exposed faults in the studied systems were subsequently confirmed and fixed by the developers

A New Method for Structural Simulation

In this paper structural change is defined and a tool to simulate structural changes is introduced which consists of a new simulation language which allows to deal separately with quantitative changes and structural qualitative changes. Two strategies of structural simulation are described. In the first one, the user defines the possible structures and conditions of change. In this case, the simulation process finds the structural paths through successive structures. In the second strategy, the structures are generated by the simulation process based on the model of creative thinking proposed by Poincare and Hadamard. AI and genetic programming techniques are used to implement the model. A simple example is given to illustrate the method of the second strategy

Deep Reinforcement Learning for Black-box Testing of Android Apps

The state space of Android apps is huge, and its thorough exploration during testing remains a significant challenge. The best exploration strategy is highly dependent on the features of the app under test. Reinforcement Learning (RL) is a machine learning technique that learns the optimal strategy to solve a task by trial and error, guided by positive or negative reward, rather than explicit supervision. Deep RL is a recent extension of RL that takes advantage of the learning capabilities of neural networks. Such capabilities make Deep RL suitable for complex exploration spaces such as one of Android apps. However, state-of-the-art, publicly available tools only support basic, Tabular RL. We have developed ARES, a Deep RL approach for black-box testing of Android apps. Experimental results show that it achieves higher coverage and fault revelation than the baselines, including state-of-the-art tools, such as TimeMachine and Q-Testing. We also investigated the reasons behind such performance qualitatively, and we have identified the key features of Android apps that make Deep RL particularly effective on them to be the presence of chained and blocking activities. Moreover, we have developed FATE to fine-tune the hyperparameters of Deep RL algorithms on simulated apps, since it is computationally expensive to carry it out on real apps

Empirical assessment of the effort needed to attack programs protected with client/server code splitting

Context. Code hardening is meant to fight malicious tampering with sensitive code executed on client hosts. Code splitting is a hardening technique that moves selected chunks of code from client to server. Although widely adopted, the effective benefits of code splitting are not fully understood and thoroughly assessed. Objective. The objective of this work is to compare non protected code vs. code splitting protected code, considering two levels of the chunk size parameter, in order to assess the effectiveness of the protection - in terms of both attack time and success rate - and to understand the attack strategy and process used to overcome the protection. Method. We conducted an experiment with master students performing attack tasks on a small application hardened with different levels of protection. Students carried out their task working at the source code level. Results. We observed a statistically significant effect of code splitting on the attack success rate that, on the average, was reduced from 89% with unprotected clear code to 52% with the most effective protection. The protection variant that moved some small-sized code chunks turned out to be more effective than the alternative moving fewer but larger chunks. Different strategies were identified yielding different success rates. Moreover, we discovered that successful attacks exhibited different process w.r.t. failed ones.Conclusions We found empirical evidence of the effect of code splitting, assessed the relative magnitude, and evaluated the influence of the chunk size parameter. Moreover, we extracted the process used to overcome such obfuscation technique

Loosening of environmental licensing threatens Brazilian biodiversity and sustainability

Environmental licensing is one of Brazil’s main environmental-policy instruments and is intended to regulate anthropogenic activities and to avoid their impacts on the environment. This licensing is now at risk to being annihilated. Bill 3729/2004 was recently approved by Brazil’s Chamber of Deputies, and if approved by the Senate (as is likely) it would create the so-called ‘general law of environmental licensing’ and a series of changes weakening environmental impact assessments, public participation and supervision by environmental agencies. The changes include creation of a self-declared license in which licenses would be issued automatically without any analysis by technical staff in the environmental agencies. Various types of small and medium-sized projects would be completely exempted from licensing. If approved, the bill would cause irreversible environmental losses to megadiverse Brazilian ecosystems and allow installation of projects with high environmental impact without any impact analysis or measures to minimize or recover from impacts or to provide environmental compensation for them

Nutritional interventions for patients with melanoma: From prevention to therapy—an update

Melanoma is an aggressive skin cancer, whose incidence rates have increased over the past few decades. Risk factors for melanoma are both intrinsic (genetic and familiar predisposition) and extrinsic (environment, including sun exposure, and lifestyle). The recent advent of targeted and immune-based therapies has revolutionized the treatment of melanoma, and research is focusing on strategies to optimize them. Obesity is an established risk factor for several cancer types, but its possible role in the etiology of melanoma is controversial. Body mass index, body surface area, and height have been related to the risk for cutaneous melanoma, although an ‘obesity paradox’ has been described too. Increasing evidence suggests the role of nutritional factors in the prevention and management of melanoma. Several studies have demonstrated the impact of dietary attitudes, specific foods, and nutrients both on the risk for melanoma and on the progression of the disease, via the effects on the oncological treatments. The aim of this narrative review was to summarize the main literature results regarding the preventive and therapeutic role of nutritional schemes, specific foods, and nutrients on melanoma incidence and progression

Finding the Optimal Balance between Over and Under Approximation of Models Inferred from Execution Logs

Models inferred from execution traces (logs) may admit more behaviours than those possible in the real system (over-approximation) or may exclude behaviours that can indeed occur in the real system (under-approximation). Both problems negatively affect model based testing. In fact, over-approximation results in infeasible test cases, i.e., test cases that cannot be activated by any input data. Under-approximation results in missing test cases, i.e., system behaviours that are not represented in the model are also never tested. In this paper we balance over- and under-approximation of inferred models by resorting to multi-objective optimization achieved by means of two search-based algorithms: A multi-objective Genetic Algorithm (GA) and the NSGA-II. We report the results on two open-source web applications and compare the multi-objective optimization to the state-of-the-art KLFA tool. We show that it is possible to identify regions in the Pareto front that contain models which violate fewer application constraints and have a higher bug detection ratio. The Pareto fronts generated by the multi-objective GA contain a region where models violate on average 2% of an application's constraints, compared to 2.8% for NSGA-II and 28.3% for the KLFA models. Similarly, it is possible to identify a region on the Pareto front where the multi-objective GA inferred models have an average bug detection ratio of 110: 3 and the NSGA-II inferred models have an average bug detection ratio of 101: 6. This compares to a bug detection ratio of 310928: 13 for the KLFA tool. © 2012 IEEE

Predictive value of baseline [18f]fdg pet/ct for response to systemic therapy in patients with advanced melanoma

Background/Aim: To evaluate the association between baseline [18F]FDG-PET/CT tumor burden parameters and disease progression rate after first-line target therapy or immunotherapy in advanced melanoma patients. Materials and Methods: Forty four melanoma patients, who underwent [18F]FDG-PET/CT before first-line target therapy (28/44) or immunotherapy (16/44), were retrospectively analyzed. Whole-body and per-district metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were calculated. Therapy response was assessed according to RECIST 1.1 on CT scan at 3 (early) and 12 (late) months. PET parameters were compared using the Mann–Whitney test. Optimal cut-offs for predicting progression were defined using the ROC curve. PFS and OS were studied using Kaplan–Meier analysis. Results: Median (IQR) MTVwb and TLGwb were 13.1 mL and 72.4, respectively. Non-responder patients were 38/44, 26/28 and 12/16 at early evaluation, and 33/44, 21/28 and 12/16 at late evaluation in the whole-cohort, target, and immunotherapy subgroup, respectively. At late evaluation, MTVbone and TLGbone were higher in non-responders compared to responder patients (all p < 0.037) in the whole-cohort and target subgroup and MTVwb and TLGwb (all p < 0.022) in target subgroup. No significant differences were found for the immunotherapy subgroup. No metabolic parameters were able to predict PFS. Controversially, MTVlfn, TLGlfn, MTVsoft + lfn, TLGsoft + lfn, MTVwb and TLGwb were significantly associated (all p < 0.05) with OS in both the whole-cohort and target therapy subgroup. Conclusions: Higher values of whole-body and bone metabolic parameters were correlated with poorer outcome, while higher values of whole-body, lymph node and soft tissue metabolic parameters were correlated with OS

A School-Based Program to Promote Well-Being in Preadolescents: Results From a Cluster Quasi-Experimental Controlled Study

Diario della Salute [My Health Diary] is a school-based program designed to enhance the subjective well-being and health of 12- to 13-year-old students. We hypothesized that providing students with the social and emotional skills to fulfill their potential and deal with common developmental tasks of adolescence (e.g., onset of puberty, identity development, increased responsibilities and academic demands) would result in improved well-being and health. The program comprises five standardized interactive lessons concerning common psychosocial and health issues in adolescence, and two narrative booklets addressed to both students and their parents. We evaluated the effectiveness of the program in terms of the students' subjective well-being, aggressive behavior, and health behavior. Using a quasi-experimental study design, schools in the intervention group implemented the full program and those in the comparison group received their regular curriculum. We administered measures of the study's objectives both before and after program implementation. Statistical analyses accounted for within-school clustering, potential socioeconomic and demographic confounding, and pre-implementation levels of these measures. We sampled 62 schools and allocated 2630 students to either an intervention or comparison group. Sociodemographic characteristics and baseline outcomes were balanced across study groups. Unexpectedly, respondents in the intervention group had 0.38 greater mean adjusted score of the WHO/Europe Health Behaviour in School-Aged Children Symptom Checklist instrument than respondents in the comparison group, indicating a reduction in subjective well-being. We did not observe any program effects on aggressive and health behaviors. The apparent reduction in subjective well-being reflected by an increased perception of psychosomatic complaints is suggestive of either increased emotional competence or, potentially, iatrogenic program effects. While greater emotional competence is positively associated with well-being over the course of life, the program in its present form should not be disseminated due to the possibility of adverse unintended effects