Amortising the Cost of Mutation Based Fault Localisation using Statistical Inference

Mutation analysis can effectively capture the dependency between source code and test results. This has been exploited by Mutation Based Fault Localisation (MBFL) techniques. However, MBFL techniques suffer from the need to expend the high cost of mutation analysis after the observation of failures, which may present a challenge for its practical adoption. We introduce SIMFL (Statistical Inference for Mutation-based Fault Localisation), an MBFL technique that allows users to perform the mutation analysis in advance against an earlier version of the system. SIMFL uses mutants as artificial faults and aims to learn the failure patterns among test cases against different locations of mutations. Once a failure is observed, SIMFL requires either almost no or very small additional cost for analysis, depending on the used inference model. An empirical evaluation of SIMFL using 355 faults in Defects4J shows that SIMFL can successfully localise up to 103 faults at the top, and 152 faults within the top five, on par with state-of-the-art alternatives. The cost of mutation analysis can be further reduced by mutation sampling: SIMFL retains over 80% of its localisation accuracy at the top rank when using only 10% of generated mutants, compared to results obtained without sampling

Hydrogen sorption mechanisms in lithium amide and metal hydride reactive systems.

PhDConsiderable effort has been devoted to the M-N-H system for solid-state hydrogen storage. However, the desorption mechanism is still unclear and the desorption temperature is too high for practical considerations. Here, the desorption characteristics of LiNH2 and a mixture of (LiNH2+LiH) were firstly comparatively studied by simultaneous then-nogravimetry, differential scanning calorimetry and mass spectrometry for further understanding of H2 desorption in the (LiNH2+LiH) system. Mass spectrometry and thermal analysis of (LiNH2+LiH) mixtures indicate that approximately 5 mass % of H2 is released at 180*C after four hours of milling without any apparent release of NH3, whereas insufficient mixing of the two compounds cannot stop the escaping of NH3 from the mixture. Non-unifon-ri mixing can lead to the escape of NH3 from the mixture. The evidence further supports the notion that NH3 intermediated reaction is a possible reaction path within the thermal desorption of the (LiNH2+ LiH) mixture. BN additive, among selected nitrides shows the best effect on desorption from (LiNH2+ LiH). (LiNH2+MgH2)materials with different molar ratios (4: 3,4: 2 and 4: 1) were also studied for their sorption properties and mechanisms. Results show that more than 6 mass% H2 is desorbed from 1500C for the (4LiNH2 +3MgH2)mixture, with two H2peaks at 200 and 320'C. Meanwhile, there is only -5 mass% for (4LiNH2 +2MgH2) mixture with one H2 peak at 200 T. Reversibility measurements suggest that LiNH2 and MgH2 cannot be recovered after absorption; instead, Li2NH and Mg(NH2)2 (or MgNH) take over to perform the H2 storage functions. The (4LiNH2+3MgH2 ) mixture possess a greater H2 capacity in first desorption, but shows less than 2 mass% reversible capacity in subsequent cycles. However, there is only about I mass% capacity loss during the reversibility measurement for the (4LiNH2 +2MgH2)mixture. Other M-N-H systems, mainly NaH, KH, AlH3 and CaH2, were also investigated, and only CaH2 shows the capability of reacting with LiNH2 to produce H2 among these candidates

The Implications Of Accounting Conservatism For The Relation Between Earnings And Stock Returns

Characterizing accounting conservatism as the accountants’ tendency to require a higher degree of verification for recognizing good news than bad news, Basu (1997) predicts that the slope coefficient and R2 in a regression of earnings on concurrent stock returns will be higher for bad news (negative stock returns) than for good news (positive stock returns). However, standard econometric analysis indicates that the R2 is a function of the sensitivity of earnings to returns and the noise ratio, which is defined as the ratio of the variance of noise in earnings to the variance of noise in returns. I show that the R2 from the regression of earnings on stock returns is not necessarily higher for bad news than for good news. So the test of R2 is not a robust test of accounting conservatism. Consistent with the prediction, I find that the slope coefficient is higher for bad news firms reporting losses than for good news firms reporting profits, but R2 is lower for bad news firms reporting losses than for good news firms reporting profits

How Different Apology Components Drive Trust Repair: The Moderating Effect of Social Value Orientation

Trust is commonly recognized as a significant part in social life. Exploring how to repair violated trust is necessary because trust transgressions occur frequently. Apologizing is one of the methods commonly used to repair trust. Rather than simply regarding it as a dichotomous phenomenon in most extant researches, additional details on the effect mechanisms of apology components for trust repair must be investigated. Thus this study explores how three apology components drive trust repair through forgiveness, considering the moderating effect of social value orientation. Forgiveness mediates the effect of compensation, empathy, and acknowledgment on trust repair, and that prosocials react more positively to the effect of acknowledgment on trust repair in forgiveness than proselfs. This study can contribute to promoting understanding on how apology really works and how to apology in accordance with people’s tendencies. Keywords：Apology components, Forgiveness, Social value orientation, Trust repai

Guiding Deep Learning System Testing using Surprise Adequacy

Deep Learning (DL) systems are rapidly being adopted in safety and security critical domains, urgently calling for ways to test their correctness and robustness. Testing of DL systems has traditionally relied on manual collection and labelling of data. Recently, a number of coverage criteria based on neuron activation values have been proposed. These criteria essentially count the number of neurons whose activation during the execution of a DL system satisfied certain properties, such as being above predefined thresholds. However, existing coverage criteria are not sufficiently fine grained to capture subtle behaviours exhibited by DL systems. Moreover, evaluations have focused on showing correlation between adversarial examples and proposed criteria rather than evaluating and guiding their use for actual testing of DL systems. We propose a novel test adequacy criterion for testing of DL systems, called Surprise Adequacy for Deep Learning Systems (SADL), which is based on the behaviour of DL systems with respect to their training data. We measure the surprise of an input as the difference in DL system's behaviour between the input and the training data (i.e., what was learnt during training), and subsequently develop this as an adequacy criterion: a good test input should be sufficiently but not overtly surprising compared to training data. Empirical evaluation using a range of DL systems from simple image classifiers to autonomous driving car platforms shows that systematic sampling of inputs based on their surprise can improve classification accuracy of DL systems against adversarial examples by up to 77.5% via retraining