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How to use get_bugs method in Kiwi

Best Python code snippet using Kiwi_python

Diagnosis_Results.py

Source:Diagnosis_Results.py

...59        failed_comps = set(self.get_components_in_failed_tests())60        metrics["num_failed_comps"] = len(failed_comps)61        metrics["only_failed_comps"] = len(failed_comps - passed_comps)62        metrics["only_passed_comps"] = len(passed_comps - failed_comps)63        metrics["num_bugs"] = len(self.get_bugs())64        metrics["wasted"] = self.calc_wasted_components()65        metrics["top_k"] = self.calc_top_k()66        metrics["num_comps_in_diagnoses"] = len(self._get_comps_in_diagnoses())67        metrics["bugs_cover_ratio"] = self._get_bugs_cover_ratio()68        metrics["average_trace_size"] = self._get_average_trace_size()69        metrics["average_component_activity"] = self._get_average_component_activity()70        metrics["average_diagnosis_size"] = self._get_average_diagnosis_size()71        metrics["bugs_scores_average"], metrics["bugs_scores_std"], metrics["bugs_scores_entropy"] = self._get_bugs_scores()72        metrics["non_bugs_scores_average"], metrics["non_bugs_scores_std"], metrics["non_bugs_scores_entropy"] = self._get_non_bugs_scores()73        metrics.update(self.cardinality())74        # metrics["ochiai"] = self.calc_ochiai_values()75        return metrics7677    def _get_comps_in_diagnoses(self):78        return reduce(set.__or__, list(map(lambda x: set(x.diagnosis), self.diagnoses)), set())7980    def _get_bugs_cover_ratio(self):81        bugs = self.get_bugs()82        comps = self._get_comps_in_diagnoses()83        return len(set(comps) & set(bugs)) / (len(bugs) * 1.0)8485    def _get_bugs_scores(self):86        bugs = self.get_bugs()87        comps_prob = dict(self.get_components_probabilities())88        bugs_prob = list(map(lambda x: comps_prob.get(x, 0), bugs))89        return np.mean(bugs_prob), np.std(bugs_prob), entropy(bugs_prob)9091    def _get_average_trace_size(self):92        return np.mean(list(map(len, self.pool.values())))9394    def _get_average_diagnosis_size(self):95        return np.mean(list(map(lambda x: len(x.diagnosis), self.diagnoses)))9697    def _get_average_component_activity(self):98        return np.mean(list(Counter(reduce(list.__add__, self.pool.values(), [])).values()))99100    def _get_non_bugs_scores(self):101        bugs = self.get_bugs()102        comps_prob = dict(self.get_components_probabilities())103        non_bugs_prob = list(map(comps_prob.get, filter(lambda c: c not in bugs, comps_prob)))104        return np.mean(non_bugs_prob), np.std(non_bugs_prob), entropy(non_bugs_prob)105106    def _get_metrics_list(self):107        return sorted(self.metrics.items(), key=lambda m:m[0])108109    def get_metrics_values(self):110        return list(map(lambda m:m[1], self._get_metrics_list()))111112    def get_metrics_names(self):113        return list(map(lambda m:m[0], self._get_metrics_list()))114115    def __repr__(self):116        return repr(self.metrics)117118    @staticmethod119    def precision_recall_for_diagnosis(buggedComps, dg, pr, validComps):120        fp = len([i1 for i1 in dg if i1 in validComps])121        fn = len([i1 for i1 in buggedComps if i1 not in dg])122        tp = len([i1 for i1 in dg if i1 in buggedComps])123        tn = len([i1 for i1 in validComps if i1 not in dg])124        if ((tp + fp) == 0):125            precision = "undef"126        else:127            precision = (tp + 0.0) / float(tp + fp)128            a = precision129            precision = precision * float(pr)130        if ((tp + fn) == 0):131            recall = "undef"132        else:133            recall = (tp + 0.0) / float(tp + fn)134            recall = recall * float(pr)135        return precision, recall136137    def calc_precision_recall(self):138        recall_accum=0139        precision_accum=0140        validComps=[x for x in set(reduce(list.__add__, self.pool.values())) if x not in self.get_bugs()]141142        self.diagnoses.sort(key = lambda x: x.probability, reverse = True)143144        self.diagnoses = self.diagnoses[:5]145146        for d in self.diagnoses:147            dg=d.diagnosis148            pr=d.probability149            precision, recall = Diagnosis_Results.precision_recall_for_diagnosis(self.get_bugs(), dg, pr, validComps)150            if(recall!="undef"):151                recall_accum=recall_accum+recall152            if(precision!="undef"):153                precision_accum=precision_accum+precision154        return precision_accum,recall_accum155156    def get_tests(self):157        return self.pool.items()158159    def get_bugs(self):160        return self.bugs161162    def get_initial_tests_traces(self):163        return list(map(lambda test: (sorted(self.pool[test]), self.error[test]), self.initial_tests))164165    def _get_tests_by_error(self, error):166        by_error = list(filter(lambda test: self.error[test] == error, self.initial_tests))167        return dict(map(lambda test: (test, self.pool[test]), by_error))168169    def get_components(self):170        return set(reduce(list.__add__, self.pool.values()))171172    def _get_components_by_error(self, error):173        return set(reduce(list.__add__, self._get_tests_by_error(error).values(), []))174175    def get_components_in_failed_tests(self):176        return self._get_components_by_error(1)177178    def get_components_in_passed_tests(self):179        return self._get_components_by_error(0)180181    def get_components_probabilities(self):182        """183        calculate for each component c the sum of probabilities of the diagnoses that include c184        return dict of (component, probability)185        """186        compsProbs={}187        for d in self.diagnoses:188            p = d.get_prob()189            for comp in d.get_diag():190                compsProbs[comp] = compsProbs.get(comp,0) + p191        return sorted(compsProbs.items(), key=lambda x: x[1], reverse=True)192193    def calc_wasted_components(self):194        components = list(map(lambda x: x[0], self.get_components_probabilities()))195        if len(self.get_bugs()) == 0:196            return len(components)197        wasted = 0.0198        for b in self.get_bugs():199            if b not in components:200                return len(components)201            wasted += components.index(b)202        return wasted / len(self.get_bugs())203204    def calc_expense(self):205        components = list(map(lambda x: x[0], self.get_components_probabilities()))206        total_expense = None207        total_bugs_in_components = 0208        for bug in self.get_bugs():209            if bug in components:210                if total_expense:211                    total_expense += ((components.index(bug)/len(components)) * 100)212                    total_bugs_in_components += 1213                else:214                    total_expense = (components.index(bug)/len(components)) * 100215                    total_bugs_in_components += 1216        if total_expense:217            total_expense /= total_bugs_in_components218        if total_expense is None:219            return 100220        return total_expense221222    def calc_exam_score(self):223        components = list(map(lambda x: x[0], self.get_components_probabilities()))224225        for component in components:226            if component in self.get_bugs():227                return ((components.index(component)/len(components)) * 100)228229        return 100230231    def calc_top_k(self):232        components = list(map(lambda x: x[0], self.get_components_probabilities()))233        top_k = None234        for bug in self.get_bugs():235            if bug in components:236                if top_k:237                    top_k = max(top_k, components.index(bug))238                else:239                    top_k = components.index(bug)240        if top_k is None:241            return len(components)242        return top_k243244    def calc_entropy(self):245        return entropy(list(map(lambda diag: diag.probability, self.diagnoses)))246247    def calc_component_entropy(self):248        return entropy(list(map(lambda x: x[1], self.get_components_probabilities())))
...

lbench

Source:lbench

...61			put_bug(mock)62	if conc:63		wait(pool)64	print(f"Elapsed time: {time.time() - start}s")65def get_bugs(verbose=True) -> dict:66	resp = req.get(f"{BASE_URL}/get")67	j = json.loads(resp.content)68	for b in j["bugs"]:69		if verbose:70			print(f"ID: {b['id']}, Body: {b['body']}")71	return j["bugs"]72def clear_bugs():73	for b in get_bugs(False):74		print(f"Deleting bug #{b['id']}...")75		resp = req.delete(f"{BASE_URL}/delete?id={b['id']}")76if __name__ == "__main__":77	arg = docopt(__doc__)78	try:79		if arg["put"]:80			upload_bugs(int(arg["<num>"]), arg["--concurrent"])81		elif arg["get"]:82			get_bugs()83		elif arg["clear"]:84			clear_bugs()85	except Exception as e:...

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