FixMiner: Mining Relevant Fix Patterns for Automated Program Repair

Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurrence of bugs across projects, the associated similar patches can be leveraged to extract generic fix actions. While the literature includes various approaches leveraging similarity among patches to guide program repair, these approaches often do not yield fix patterns that are tractable and reusable as actionable input to APR systems. In this paper, we propose a systematic and automated approach to mining relevant and actionable fix patterns based on an iterative clustering strategy applied to atomic changes within patches. The goal of FixMiner is thus to infer separate and reusable fix patterns that can be leveraged in other patch generation systems. Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree structure of the edit scripts that captures the AST-level context of the code changes. FixMiner uses different tree representations of Rich Edit Scripts for each round of clustering to identify similar changes. These are abstract syntax trees, edit actions trees, and code context trees. We have evaluated FixMiner on thousands of software patches collected from open source projects. Preliminary results show that we are able to mine accurate patterns, efficiently exploiting change information in Rich Edit Scripts. We further integrated the mined patterns to an automated program repair prototype, PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J benchmark. Beyond this quantitative performance, we show that the mined fix patterns are sufficiently relevant to produce patches with a high probability of correctness: 81% of PARFixMiner's generated plausible patches are correct.Comment: 31 pages, 11 figure

Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs

Conserved collateral antibiotic susceptibility networks in diverse clinical strains of Escherichia coli.

There is urgent need to develop novel treatment strategies to reduce antimicrobial resistance. Collateral sensitivity (CS), where resistance to one antimicrobial increases susceptibility to other drugs, might enable selection against resistance during treatment. However, the success of this approach would depend on the conservation of CS networks across genetically diverse bacterial strains. Here, we examine CS conservation across diverse Escherichia coli strains isolated from urinary tract infections. We determine collateral susceptibilities of mutants resistant to relevant antimicrobials against 16 antibiotics. Multivariate statistical analyses show that resistance mechanisms, in particular efflux-related mutations, as well as the relative fitness of resistant strains, are principal contributors to collateral responses. Moreover, collateral responses shift the mutant selection window, suggesting that CS-informed therapies may affect evolutionary trajectories of antimicrobial resistance. Our data allow optimism for CS-informed therapy and further suggest that rapid detection of resistance mechanisms is important to accurately predict collateral responses

Collateral sensitivity in clinical Escherichia coli isolates

Background At present time, antimicrobial resistance is emerging more rapidly than the development of novel antimicrobials, presenting a serious threat to how we prevent and treat infectious diseases. Several treatment strategies to counteract this development have been proposed, among these is the use of collateral sensitivity in clinical treatment. The ability to predict collateral sensitivity and cross-resistance effects is essential to exploiting this concept. In this study, we aimed to investigate the patterns of collateral sensitivity and cross-resistance in ciprofloxacin resistant isolates carrying gyrA and parC mutations. Method Ciprofloxacin resistant isolates were evolved from three clinical E. coli strain using static and dynamic selection methods. Isolates were selected based on identified mutations and level of ciprofloxacin resistance measured with diffusion gradient strips. DNA sequencing was used to detect mutations in gyrA and parC. Resistant isolates carrying at least one gyrA and parC mutation were characterized by IC90 assays with micro-broth dilutions of six unrelated antimicrobial agents. The observed collateral sensitivity and cross-resistance effects were displayed in a heat map. Results Various non-synonymous point mutations in gyrA and parC were identified in several of the generated ciprofloxacin resistant isolates. These mutants displayed collateral sensitivity and cross-resistance to several unrelated antimicrobials. Collateral sensitivity to gentamicin and trimethoprim was observed in the majority isolates. Cross-resistance effects were found in several mutants, specifically to ceftazidime, chloramphenicol and colistin. Conclusion Our findings suggest that ciprofloxacin resistant mutants with gyrA and parC mutations display a clear tendency of collateral sensitivity to gentamicin, an effect which potentially can be exploited in future treatment. However, we propose further investigation into specific point mutations within these genes, to better understand the observed variations in collateral sensitivity and cross-resistance

The Impact of Collateral Evolution on Optimal Dosing Strategies and Evolution on Paired Fitness Landscapes

Drug resistance is an ever-growing threat to successful treatment of bacterial, cancer and viral infections. As pathogens and cancers continue to find evolutionary solutions to the drugs we treat them with, scientists have begun to focus on more evolutionary-based therapies such as drug cycling. These therapies aim to constrain or control evolution in a particular way such that intractable resistance never evolves. In the same vein, recent work has revealed collateral sensitivity as a promising avenue to guide evolution away from untreatable resistance states. Collateral evolution occurs when a population evolves resistance to the selecting drug and this mechanism of resistance confers "collateral" effects to different drugs it is not exposed to. In this work we show how collateral profiles might be used to slow the acquisition to resistance in a simplified laboratory-based evolution experiment. We demonstrate that intuitive cycling protocols often fail over long time periods, whereas mathematically optimized protocols maintain long-term sensitivity at the cost of transient periods of high resistance. We then extend this work to include nonantibiotic stressors such as pH, salt and food preservatives. This extension highlights that more work is necessary to understand the role these common environments have on the development of multidrug resistance. Finally, using the well-known fitness landscape paradigm, we explore how collateral effects influence the evolutionary dynamics of a pair of landscapes with tunable correlations. We show that alternating evolution in highly correlated environments can lead to higher mean fitness than evolution in either landscape alone, while alternating between two anti-correlated landscapes results in a lower mean fitness. We demonstrate this is due to the location and number of shared maxima between the two correlated landscapes, which change as a function of ruggedness (epistasis) and paired landscape correlation. Taken together, these results begin to answer many of the important questions required to translate collateral sensitivity into clinical treatments.PHDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163088/1/jamaltas_1.pd

Collateral sensitivity in clinical mecillinam resistant isolates of Escherichia coli

Background The rapid increase in antimicrobial resistance (AMR) has become a major threat to the successful management of infectious diseases. To counteract this global threat, development of novel treatment strategies is essential. A promising strategy may be exploiting collateral sensitivity; a phenomenon that occurs when a microorganism that has developed resistance to one antimicrobial agent, exhibits increased susceptibility to another antimicrobial agent. In order to develop novel treatment strategies and prevent further resistance development, we aimed to explore the generality of the concept of collateral sensitivity in clinical urinary tract isolates of E. coli. Furthermore, we wanted to investigate the underlying mechanisms of collateral sensitivity. Methods We evolved resistance to mecillinam in a collection of clinical isolates of E. coli. Ten were selected for further determination of possible collateral sensitivity and cross-resistance networks. The IC90-assay with micro broth dilution was used for this purpose, which we tested for eight different antimicrobial agents. The results were displayed in heat maps and graphs showing the distribution of AMR to various agents. PCR and DNA sequencing were performed for the mrdA gene to detect mutations that may confer mecillinam resistance. Results According to our results both collateral sensitivity and cross-resistance occurred in mecillinam resistant isolates. Chloramphenicol presented the highest tendency of collateral sensitivity, while ciprofloxacin presented the highest tendency of cross-resistance. In general, a substantial tendency for collateral sensitivity frequently appeared compared to cross-resistance. Moreover, 13 synonymous point mutations were observed in the mrdA gene, leading to no alteration in the amino acid sequence. Conclusion Based on our in vitro results, we suggest mecillinam could be a good candidate to be employed as the first drug of choice for UTIs caused by E. coli. Mecillinam resistant isolates exhibited a clear tendency for collateral sensitivity, which we believe would occur on the population level as well. Further investigations of the underlying mechanisms of collateral sensitivity are required

A Model to Estimate First-Order Mutation Coverage from Higher-Order Mutation Coverage

The test suite is essential for fault detection during software development. First-order mutation coverage is an accurate metric to quantify the quality of the test suite. However, it is computationally expensive. Hence, the adoption of this metric is limited. In this study, we address this issue by proposing a realistic model able to estimate first-order mutation coverage using only higher-order mutation coverage. Our study shows how the estimation evolves along with the order of mutation. We validate the model with an empirical study based on 17 open-source projects.Comment: 2016 IEEE International Conference on Software Quality, Reliability, and Security. 9 page