The effect of gibberellic acid applications on the cracking rate and fruit quality in the ‘0900 Ziraat’ sweet cherry cultivar

This study was conducted to determine the effects of different gibberellic acid (GA3) doses (0, 5, 10, 15, 20 and 25 ppm) on the fruit quality and cracking rate in the ‘0900 Ziraat’ sweet cherry cultivar. In this study, different GA3 doses affected significantly (p < 0.05) the most important characteristics of fruit such as fruit weight, fruit firmness and cracking rate determining the marketable value. The lowest and highest fruit weight was 7.95 and 10.02 g in control and 15 ppm GA3 treatments, respectively. Similarly, the lowest and highest fruit firmness was found to be 7.45 and 9.63 N in control treatment and 15 ppm GA3 treatments, respectively. In addition, cracking index of 5.60 and 25.50% was obtained for 20 ppm GA3 and control treatments, respectively. It was also found that GA3 treatments delayed the harvest date for 3 - 4 days and increased the fruit weight by 10.71% in comparison with the control. Furthermore, the application of GA3 decreased the fruit cracking rate by 77.80% in comparison with the control. Fruit colour values were also affected by GA3, application, and brighter and darker red coloured fruits were obtained.Key words: Sweet cherry, gibberellic acid, cracking index, fruit qualit

You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems

Properly benchmarking Automated Program Repair (APR) systems should contribute to the development and adoption of the research outputs by practitioners. To that end, the research community must ensure that it reaches significant milestones by reliably comparing state-of-the-art tools for a better understanding of their strengths and weaknesses. In this work, we identify and investigate a practical bias caused by the fault localization (FL) step in a repair pipeline. We propose to highlight the different fault localization configurations used in the literature, and their impact on APR systems when applied to the Defects4J benchmark. Then, we explore the performance variations that can be achieved by `tweaking' the FL step. Eventually, we expect to create a new momentum for (1) full disclosure of APR experimental procedures with respect to FL, (2) realistic expectations of repairing bugs in Defects4J, as well as (3) reliable performance comparison among the state-of-the-art APR systems, and against the baseline performance results of our thoroughly assessed kPAR repair tool. Our main findings include: (a) only a subset of Defects4J bugs can be currently localized by commonly-used FL techniques; (b) current practice of comparing state-of-the-art APR systems (i.e., counting the number of fixed bugs) is potentially misleading due to the bias of FL configurations; and (c) APR authors do not properly qualify their performance achievement with respect to the different tuning parameters implemented in APR systems.Comment: Accepted by ICST 201

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

TBar: Revisiting Template-based Automated Program Repair

We revisit the performance of template-based APR to build comprehensive knowledge about the effectiveness of fix patterns, and to highlight the importance of complementary steps such as fault localization or donor code retrieval. To that end, we first investigate the literature to collect, summarize and label recurrently-used fix patterns. Based on the investigation, we build TBar, a straightforward APR tool that systematically attempts to apply these fix patterns to program bugs. We thoroughly evaluate TBar on the Defects4J benchmark. In particular, we assess the actual qualitative and quantitative diversity of fix patterns, as well as their effectiveness in yielding plausible or correct patches. Eventually, we find that, assuming a perfect fault localization, TBar correctly/plausibly fixes 74/101 bugs. Replicating a standard and practical pipeline of APR assessment, we demonstrate that TBar correctly fixes 43 bugs from Defects4J, an unprecedented performance in the literature (including all approaches, i.e., template-based, stochastic mutation-based or synthesis-based APR).Comment: Accepted by ISSTA 201

Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma

BACKGROUNDPatients with p16+ oropharyngeal squamous cell carcinoma (OPSCC) are potentially cured with definitive treatment. However, there are currently no reliable biomarkers of treatment failure for p16+ OPSCC. Pathologist-based visual assessment of tumor cell multinucleation (MN) has been shown to be independently prognostic of disease-free survival (DFS) in p16+ OPSCC. However, its quantification is time intensive, subjective, and at risk of interobserver variability.METHODSWe present a deep-learning-based metric, the multinucleation index (MuNI), for prognostication in p16+ OPSCC. This approach quantifies tumor MN from digitally scanned H&E-stained slides. Representative H&E-stained whole-slide images from 1094 patients with previously untreated p16+ OPSCC were acquired from 6 institutions for optimization and validation of the MuNI.RESULTSThe MuNI was prognostic for DFS, overall survival (OS), or distant metastasis-free survival (DMFS) in p16+ OPSCC, with HRs of 1.78 (95% CI: 1.37-2.30), 1.94 (1.44-2.60), and 1.88 (1.43-2.47), respectively, independent of age, smoking status, treatment type, or tumor and lymph node (T/N) categories in multivariable analyses. The MuNI was also prognostic for DFS, OS, and DMFS in patients with stage I and stage III OPSCC, separately.CONCLUSIONMuNI holds promise as a low-cost, tissue-nondestructive, H&E stain-based digital biomarker test for counseling, treatment, and surveillance of patients with p16+ OPSCC. These data support further confirmation of the MuNI in prospective trials.FUNDINGNational Cancer Institute (NCI), NIH; National Institute for Biomedical Imaging and Bioengineering, NIH; National Center for Research Resources, NIH; VA Merit Review Award from the US Department of VA Biomedical Laboratory Research and Development Service; US Department of Defense (DOD) Breast Cancer Research Program Breakthrough Level 1 Award; DOD Prostate Cancer Idea Development Award; DOD Lung Cancer Investigator-Initiated Translational Research Award; DOD Peer-Reviewed Cancer Research Program; Ohio Third Frontier Technology Validation Fund; Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering; Clinical and Translational Science Award (CTSA) program, Case Western Reserve University; NCI Cancer Center Support Grant, NIH; Career Development Award from the US Department of VA Clinical Sciences Research and Development Program; Dan L. Duncan Comprehensive Cancer Center Support Grant, NIH; and Computational Genomic Epidemiology of Cancer Program, Case Comprehensive Cancer Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the US Department of VA, the DOD, or the US Government

Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. While the state of the art explore research directions that require dynamic information or rely on manually-crafted heuristics, we study the benefit of learning code representations to learn deep features that may encode the properties of patch correctness. Our work mainly investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations. We report on findings based on embeddings produced by pre-trained and re-trained neural networks. Experimental results demonstrate the potential of embeddings to empower learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based embeddings associated with logistic regression yielded an AUC value of about 0.8 in predicting patch correctness on a deduplicated dataset of 1000 labeled patches. Our study shows that learned representations can lead to reasonable performance when comparing against the state-of-the-art, PATCH-SIM, which relies on dynamic information. These representations may further be complementary to features that were carefully (manually) engineered in the literature