Job: Testing Engineer

 

 Job Description:

 The primary objective of System Test is to ensure the individual products combine to form a high quality solution, which will provide customer satisfaction. You would work closely with the Test Lead and the project team to ensure that there is a consistent and coherent test strategy across the solution.

 

 You would be involved in the:

  -  evaluation of product and system requirements

  -  definition of an overall test strategy

  -  development of test plan/case/script/data

  -  execution of solution test plan

  -  installation, commissioning and beta testing

  -   work closely with the project manager, developers, QA analysts, and other team members to test project deliverables.

 

 

 Qualifications preferred:

 Bachelor degree in a computing, science or engineering subject.

 

 Skills/Experience required:

  -  2+ years experience in software development or software test, experience in middle-ware testing is better;

  -  Experience with JMeter / WinRuner automation tool;

<!--[if !supportLists]-->  -  A keen problem-solver with a proactive, team-oriented approach.

 

 Desired skills:

  -  Experience of testing real-time distributed software / Network Management System

  -  Telecoms : Knowledge of Telecoms, SS7, IP and other hybrid interfaces and the technology behind them (E1/T1, 10/100, STM1o, ISDN, GSM,GPRS, UMTS/

  -  Knowledge of Unix, Linux, Java, C skills.

<!--[if !supportLists]-->  -  Familiarity with quality control process;

<!--[if !supportLists]-->  -  <!--[endif]-->Familiarity with network protocol

 

 Personal Qualities:

  -  Excellent inter-personal and communications skills.

  -  Commitment to deliver quality products to meet customer requirements




DMX公司是一家新加坡的上市公司,总部在香港,中国大陆、印度、新加坡都有多家分公司,主要业务是IPTV 。广州这边是研发中心,气氛还不错。

公司的网址是: www.dmx.com.hk

待遇方面:全额购买社保和住房公积金,还有餐费的一些补贴和部分车费的报销。

工作地点:广州

有兴趣的可以在这里留言或者发email 给我。


AttributeError: 'numpy.ndarray' object has no attribute 'fillna' 2025-07-19 17:07:14,762 - INFO - 目标分布: 0=713, 1=16 2025-07-19 17:07:14,770 - ERROR - 特征工程失败: 'numpy.ndarray' object has no attribute 'fillna' Traceback (most recent call last): File "d:\股票量化数据库\股票量化数据库\untitled4.py", line 232, in transform df['RSI14'] = ta.RSI(df_temp['close'].values, timeperiod=14).fillna(50) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'numpy.ndarray' object has no attribute 'fillna' 2025-07-19 17:07:14,778 - INFO - 目标分布: 0=724, 1=5 处理股票数据: 100%|██████████| 6723/6723 [02:17<00:00, 48.99it/s] 2025-07-19 17:07:19,387 - INFO - 数据集准备完成,样本数: 4285658 2025-07-19 17:07:20,151 - INFO - 目标分布: 0=4171023, 1=114635 2025-07-19 17:07:21,795 - INFO - 开始训练模型... 2025-07-19 17:07:22,508 - INFO - 类别不平衡处理: 正样本权重 = 36.39 2025-07-19 17:07:22,509 - INFO - 执行特征选择... [LightGBM] [Info] Number of positive: 114635, number of negative: 4171023 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.021112 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 2550 [LightGBM] [Info] Number of data points in the train set: 4285658, number of used features: 10 [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.026749 -> initscore=-3.594163 [LightGBM] [Info] Start training from score -3.594163 2025-07-19 17:07:31,028 - INFO - 特征重要性: volatility 563 price_change 499 volume 491 volume_change 339 MA20 318 MA5 226 low 175 high 144 open 135 close 110 RSI_diff 0 day_of_week 0 advance_decline 0 MACD_RSI 0 price_vol_ratio 0 MACD_hist 0 VWAP 0 ATR14 0 MOM10 0 cluster 0 RSI14 0 month 0 2025-07-19 17:07:31,028 - INFO - 选择前 15 个特征: ['volatility', 'price_change', 'volume', 'volume_change', 'MA20', 'MA5', 'low', 'high', 'open', 'close', 'RSI_diff', 'day_of_week', 'advance_decline', 'MACD_RSI', 'price_vol_ratio'] 2025-07-19 17:07:32,098 - INFO - 开始参数搜索... Fitting 3 folds for each of 20 candidates, totalling 60 fits Traceback (most recent call last): File D:\Anaconda\Lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec exec(code, globals, locals) File d:\股票量化数据库\股票量化数据库\untitled4.py:687 main() File d:\股票量化数据库\股票量化数据库\untitled4.py:655 in main model = trainer.train_model(X_train, y_train, groups) File d:\股票量化数据库\股票量化数据库\untitled4.py:543 in train_model search.fit(X_selected, y) File D:\Anaconda\Lib\site-packages\sklearn\model_selection\_search.py:874 in fit self._run_search(evaluate_candidates) File D:\Anaconda\Lib\site-packages\sklearn\model_selection\_search.py:1768 in _run_search evaluate_candidates( File D:\Anaconda\Lib\site-packages\sklearn\model_selection\_search.py:821 in evaluate_candidates out = parallel( File D:\Anaconda\Lib\site-packages\sklearn\utils\parallel.py:63 in __call__ return super().__call__(iterable_with_config) File D:\Anaconda\Lib\site-packages\joblib\parallel.py:1098 in __call__ self.retrieve() File D:\Anaconda\Lib\site-packages\joblib\parallel.py:975 in retrieve self._output.extend(job.get(timeout=self.timeout)) File D:\Anaconda\Lib\site-packages\joblib\_parallel_backends.py:567 in wrap_future_result return future.result(timeout=timeout) File D:\Anaconda\Lib\concurrent\futures\_base.py:456 in result return self.__get_result() File D:\Anaconda\Lib\concurrent\futures\_base.py:401 in __get_result raise self._exception MemoryError: Unable to allocate 54.5 MiB for an array with shape (5, 1428553) and data type int64
最新发布
07-20
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