發(fā)布時間：2018-06-16 04:45

  本文選題：移動應用 + 功能描述��； 參考：《大連理工大學》2016年碩士論文

【摘要】：隨著Android操作系統(tǒng)的快速發(fā)展,Android應用更新的速度也越來越快,下載量也飛速增長,Google Play商店中移動應用數(shù)目已超過200萬,為了便于用戶了解應用情況,Google Play商店為每個應用提供了一個介紹頁面,通過該頁面,用戶可以了解到該應用的詳細信息,包括名稱、開發(fā)者、截圖、功能描述、評論等。為了吸引下載量,很多應用商店會提供一些關于如何書寫好的應用功能描述的指導和建議,但是這些建議大多比較抽象或概括,而且也很難定義功能描述的質量好與不好。同時,目前暫沒有相關工具自動化地對功能描述的質量進行評估并給出建議。為了評估移動應用功能描述的質量,本文使用了數(shù)據(jù)驅動的方式構建特征,進而訓練評估模型。具體做法是從Google Play中確定了音像(Music Audio)、新聞(News Magazines)、攝影(Photography)、旅行(Travel Local)、天氣(Weather)5個類別,從中各選擇100個Android應用(Android Applications,以下簡稱Apps)的功能描述作為樣本,然后邀請30個志愿者對樣本中功能描述的質量進行評分并說明原因,進而通過對原因的過濾分析,從中構建特征,再結合志愿者的評分訓練機器學習模型。通過對志愿者評分原因的分析,我們構建了共計16個特征,結合我們的經(jīng)驗和數(shù)據(jù)特點以及相關工作確定了各個特征的計算方式,得到的特征值作為輸入值；將志愿者對功能描述的評分映射到不同的質量水平(好-Good,中-Neutral,差-Bad),并以此作為輸出值,選取了支持向量機(SVM)、決策樹(Decision Tree)、隨機森林(Random Forest)和邏輯回歸(Logistics)模型對所有數(shù)據(jù)訓練測試,進而分析各樣本數(shù)據(jù)在不同模型上的表現(xiàn)。最終音像(Music Audio)類別的樣本數(shù)據(jù)在SVM模型上取得了58%的分類準確率。另外,由于訓練模型使用的特征均是我們自己構建,我們對構建的所有特征進行了重要性分析,對比LibSVM模型的特征選擇工具和Weka中C4.5決策樹模型得到的結果,我們發(fā)現(xiàn)在特征重要性排序方面,無監(jiān)督的學習方式和有監(jiān)督的學習方式得到的結果基本一致,該結果中有幾個特征對于所有樣本數(shù)據(jù)都比較重要：功能描述長度(單詞數(shù))、每個單詞難易度(每個單詞字符個數(shù))、句子長度以及功能描述中應用特征的描述與全部描述的比例,我們希望這個結果對移動應用的開發(fā)者有一定啟示,即準備移動應用的功能描述時,應注意描述文本的長度、是否易懂以及移動應用的特征描述所占總文本的比例。
[Abstract]:With the rapid development of the Android operating system, the number of mobile apps in the Android play store has exceeded 2 million. The Google play Store provides an introduction page for each application, through which the user can learn the details of the application, including name, developer, screenshot, function description, comment, etc. In order to attract downloads, many app stores provide guidance and advice on how to write a good description of the application's functions, but most of these suggestions are abstract or general, and it is difficult to define whether the quality of the description is good or bad. At present, there are no tools to automatically evaluate the quality of function description and give suggestions. In order to evaluate the quality of functional description of mobile applications, this paper uses a data-driven approach to construct features and then train the evaluation model. This is done by identifying from Google play the five categories of AudioMusic Audio, News Magazinesh, Photography, Travel Local Travel, Weather Weather, and selecting 100 Android applications (hereinafter referred to as Apps) as a sample of their functional descriptions. Then 30 volunteers were invited to score the quality of the function description in the sample and explain the reason. Then through the filtering analysis of the reason the characteristics were constructed and the machine learning model was combined with the evaluation training of the volunteer. Based on the analysis of the cause of volunteer scoring, we constructed a total of 16 features, combined with our experience and data characteristics and related work to determine the calculation method of each feature, the obtained feature value as input value; The scores of function description of volunteers were mapped to different quality levels (good, medium neutral, poor Badn), and as output values, support vector machine (SVM), decision tree (decision tree), random forest random (Random Forest) and logical regression logistic models were selected to test all data training. Then the performance of each sample data in different models is analyzed. Finally, the classification accuracy of audio-video audio-audio category is 58%. In addition, because the features used in the training model are all constructed by ourselves, we analyze the importance of all the features constructed, and compare the feature selection tool of LibSVM model with the results obtained from the C4.5 decision tree model in Weka. We find that the results of unsupervised learning and supervised learning are basically the same as those of supervised learning. There are several features in the result that are important for all sample data: length of function description (number of words), ease of each word (number of characters per word), length of sentence and description of features used in functional description The ratio of the full description, We hope that this result will enlighten the developers of mobile applications, that is, when preparing the functional description of mobile applications, we should pay attention to the length of the description text, whether it is easy to understand and the proportion of the feature description of the mobile application to the total text.
【學位授予單位】：大連理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP316;TP181

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